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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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Chybowski B, Klimes P, Cimbalnik J, Travnicek V, Nejedly P, Pail M, Peter-Derex L, Hall J, Dubeau F, Jurak P, Brazdil M, Frauscher B. Timing matters for accurate identification of the epileptogenic zone. Clin Neurophysiol 2024; 161:1-9. [PMID: 38430856 DOI: 10.1016/j.clinph.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 12/12/2023] [Accepted: 01/01/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVE Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning models open promising avenues for improvement of epilepsy surgery evaluation. Currently, most studies restrict their analysis to short segments of intracranial EEG (iEEG). METHODS We used 2381 hours of iEEG data from 25 patients to systematically select 5-minute segments across various interictal conditions. Then, we tested machine learning models for EZ localization using iEEG features calculated within these individual segments or across them and evaluated the performance by the area under the precision-recall curve (PRAUC). RESULTS On average, models achieved a score of 0.421 (the result of the chance classifier was 0.062). However, the PRAUC varied significantly across the segments (0.323-0.493). Overall, NREM sleep achieved the highest scores, with the best results of 0.493 in N2. When using data from all segments, the model performed significantly better than single segments, except NREM sleep segments. CONCLUSIONS The model based on a short segment of iEEG recording can achieve similar results as a model based on prolonged recordings. The analyzed segment should, however, be carefully and systematically selected, preferably from NREM sleep. SIGNIFICANCE Random selection of short iEEG segments may give rise to inaccurate localization of the EZ.
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Affiliation(s)
- Bartlomiej Chybowski
- University of Edinburgh, School of Medicine, Deanery of Clinical Sciences, 47 Little France Crescent, EH164TJ Edinburgh, Scotland
| | - Petr Klimes
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic
| | - Jan Cimbalnik
- International Clinical Research Center, St. Anne's University Hospital, Pekařská 53, 602 00 Brno, Czech Republic
| | - Vojtech Travnicek
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital, Pekařská 53, 602 00 Brno, Czech Republic
| | - Petr Nejedly
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic
| | - Martin Pail
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic; Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, Member of ERN-EpiCARE, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; Behavioral and Social Neuroscience Research Group, CEITEC Central European Institute of Technology, Masaryk University, Žerotínovo nám 617/9, 601 77 Brno, Czech Republic
| | - Laure Peter-Derex
- Center for Sleep Medicine and Respiratory Diseases, Lyon University Hospital, Lyon 1 University, 103 Grande Rue de la Croix-Rousse, 69004 Lyon, France; Lyon Neuroscience Research Center, CH Le Vinatier - Bâtiment 462 - Neurocampus, 95 Bd Pinel, 69500 Lyon, France
| | - Jeff Hall
- Montreal Neurological Hospital, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Hospital, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada
| | - Pavel Jurak
- Institute of Scientific Instruments of the CAS, v. v. i., Královopolská 147, 612 00 Brno, Czech Republic
| | - Milan Brazdil
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, Member of ERN-EpiCARE, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; Behavioral and Social Neuroscience Research Group, CEITEC Central European Institute of Technology, Masaryk University, Žerotínovo nám 617/9, 601 77 Brno, Czech Republic
| | - Birgit Frauscher
- Montreal Neurological Hospital, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada; Department of Neurology, Duke University Medical School and Department of Biomedical Engineering, Pratt School of Engineering, 2424 Erwin Road, Durham, NC, 27705, USA.
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Ye X, Hu P, Yang B, Yang Y, Gao D, Zeng GQ, Wang K. Using scalp EEG to predict seizure recurrence and electrical status epilepticus in children with idiopathic focal epilepsy. Seizure 2024; 118:8-16. [PMID: 38613879 DOI: 10.1016/j.seizure.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/17/2024] [Accepted: 03/27/2024] [Indexed: 04/15/2024] Open
Abstract
PURPOSE Some individuals with idiopathic focal epilepsy (IFE) experience recurring seizures accompanied by the evolution of electrical status epilepticus during sleep (ESES). Here, we aimed to develop a predictor for the early detection of seizure recurrence with ESES in children with IFE using resting state electroencephalogram (EEG) data. METHODS The study group included 15 IFE patients who developed seizure recurrence with ESES. There were 17 children in the control group who did not experience seizure recurrence with ESES during at least 2-year follow-up. We used the degree value of the partial directed coherence (PDC) from the EEG data to predict seizure recurrence with ESES via 6 machine learning (ML) algorithms. RESULTS Among the models, the Xgboost Classifier (XGBC) model achieved the highest specificity of 0.90, and a remarkable sensitivity and accuracy of 0.80 and 0.85, respectively. The CATC showed balanced performance with a specificity of 0.85, sensitivity of 0.73, and an accuracy of 0.80, with an AUC equal to 0.78. For both of these models, F4, Fz and T4 were the overlaps of the top 4 features. CONCLUSIONS Considering its high classification accuracy, the XGBC model is an effective and quantitative tool for predicting seizure recurrence with ESES evolution in IFE patients. We developed an ML-based tool for predicting the development of IFE using resting state EEG data. This could facilitate the diagnosis and treatment of patients with IFE.
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Affiliation(s)
- Xiaofei Ye
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Neurology, Children's Hospital of Fudan University/Anhui Hospital, Hefei, China
| | - Panpan Hu
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bin Yang
- Department of Neurology, Children's Hospital of Fudan University/Anhui Hospital, Hefei, China
| | - Yang Yang
- Department of Neurology, Children's Hospital of Fudan University/Anhui Hospital, Hefei, China
| | - Ding Gao
- Department of Neurology, Children's Hospital of Fudan University/Anhui Hospital, Hefei, China
| | - Ginger Qinghong Zeng
- Institute of Advanced Technology, University of Science and Technology of China, Hefei, China.
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China.
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Karimi-Rouzbahani H, McGonigal A. Generalisability of epileptiform patterns across time and patients. Sci Rep 2024; 14:6293. [PMID: 38491096 PMCID: PMC10942983 DOI: 10.1038/s41598-024-56990-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 03/13/2024] [Indexed: 03/18/2024] Open
Abstract
The complexity of localising the epileptogenic zone (EZ) contributes to surgical resection failures in achieving seizure freedom. The distinct patterns of epileptiform activity during interictal and ictal phases, varying across patients, often lead to suboptimal localisation using electroencephalography (EEG) features. We posed two key questions: whether neural signals reflecting epileptogenicity generalise from interictal to ictal time windows within each patient, and whether epileptiform patterns generalise across patients. Utilising an intracranial EEG dataset from 55 patients, we extracted a large battery of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals during interictal and ictal windows. Our features (n = 34) quantified many aspects of the signals including statistical moments, complexities, frequency-domain and cross-channel network attributes. Decision tree classifiers were then trained and tested on distinct time windows and patients to evaluate the generalisability of epileptogenic patterns across time and patients, respectively. Evidence strongly supported generalisability from interictal to ictal time windows across patients, particularly in signal power and high-frequency network-based features. Consistent patterns of epileptogenicity were observed across time windows within most patients, and signal features of epileptogenic regions generalised across patients, with higher generalisability in the ictal window. Signal complexity features were particularly contributory in cross-patient generalisation across patients. These findings offer insights into generalisable features of epileptic neural activity across time and patients, with implications for future automated approaches to supplement other EZ localisation methods.
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Affiliation(s)
- Hamid Karimi-Rouzbahani
- Neurosciences Centre, Mater Hospital, South Brisbane, 4101, Australia.
- Mater Research Institute, University of Queensland, South Brisbane, 4101, Australia.
- Queensland Brain Institute, University of Queensland, St Lucia, 4072, Australia.
| | - Aileen McGonigal
- Neurosciences Centre, Mater Hospital, South Brisbane, 4101, Australia
- Mater Research Institute, University of Queensland, South Brisbane, 4101, Australia
- Queensland Brain Institute, University of Queensland, St Lucia, 4072, Australia
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Weiss SA, Fried I, Engel J, Bragin A, Wang S, Sperling MR, Wong RK, Nir Y, Staba RJ. Pathological neurons generate ripples at the UP-DOWN transition disrupting information transfer. Epilepsia 2024; 65:362-377. [PMID: 38041560 PMCID: PMC10922301 DOI: 10.1111/epi.17845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/03/2023]
Abstract
OBJECTIVE To confirm and investigate why pathological high-frequency oscillations (pHFOs), including ripples (80-200 Hz) and fast ripples (200-600 Hz), are generated during the UP-DOWN transition of the slow wave and if information transmission mediated by ripple temporal coupling is disrupted in the seizure-onset zone (SOZ). METHODS We isolated 217 total units from 175.95 intracranial electroencephalography (iEEG) contact-hours of synchronized macro- and microelectrode recordings from 6 patients. Sleep slow oscillation (.1-2 Hz) epochs were identified in the iEEG recording. iEEG HFOs that occurred superimposed on the slow wave were transformed to phasors and adjusted by the phase of maximum firing in nearby units (i.e., maximum UP). We tested whether, in the SOZ, HFOs and associated action potentials (APs) occur more often at the UP-DOWN transition. We also examined ripple temporal correlations using cross-correlograms. RESULTS At the group level in the SOZ, HFO and HFO-associated AP probability was highest during the UP-DOWN transition of slow wave excitability (p < < .001). In the non-SOZ, HFO and HFO-associated AP was highest during the DOWN-UP transition (p < < .001). At the unit level in the SOZ, 15.6% and 20% of units exhibited more robust firing during ripples (Cohen's d = .11-.83) and fast ripples (d = .36-.90) at the UP-DOWN transition (p < .05 f.d.r. corrected), respectively. By comparison, also in the SOZ, 6.6% (d = .14-.30) and 8.5% (d = .33-.41) of units had significantly less firing during ripples and fast ripples at the UP-DOWN transition, respectively. Additional data shows that ripple and fast ripple temporal correlations, involving global slow waves, between the hippocampus, entorhinal cortex, and parahippocampal gyrus were reduced by >50% in the SOZ compared to the non-SOZ (N = 3). SIGNIFICANCE The UP-DOWN transition of slow wave excitability facilitates the activation of pathological neurons to generate pHFOs. Ripple temporal correlations across brain regions may be important in memory consolidation and are disrupted in the SOZ, perhaps by pHFO generation.
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Affiliation(s)
- Shennan A Weiss
- Dept. of Neurology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, USA
| | - Itzhak Fried
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Jerome Engel
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Anatol Bragin
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Shuang Wang
- Depts of Neurology, Epilepsy Center, Second Affiliated Hospital of Medical College, Zhejiang University, Zhejiang, China
| | - Michael R. Sperling
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Robert K.S. Wong
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
| | - Yuval Nir
- Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- The Sieratzki-Sagol Center for Sleep Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
| | - Richard J Staba
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
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Ye H, Ye L, Hu L, Yang Y, Ge Y, Chen R, Wang S, Jin B, Ming W, Wang Z, Xu S, Xu C, Wang Y, Ding Y, Zhu J, Ding M, Chen Z, Wang S, Chen C. Widespread slow oscillations support interictal epileptiform discharge networks in focal epilepsy. Neurobiol Dis 2024; 191:106409. [PMID: 38218457 DOI: 10.1016/j.nbd.2024.106409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 01/01/2024] [Accepted: 01/09/2024] [Indexed: 01/15/2024] Open
Abstract
Interictal epileptiform discharges (IEDs) often co-occur across spatially-separated cortical regions, forming IED networks. However, the factors prompting IED propagation remain unelucidated. We hypothesized that slow oscillations (SOs) might facilitate IED propagation. Here, the amplitude and phase synchronization of SOs preceding propagating and non-propagating IEDs were compared in 22 patients with focal epilepsy undergoing intracranial electroencephalography (EEG) evaluation. Intracranial channels were categorized into the irritative zone (IZ) and normal zone (NOZ) regarding the presence of IEDs. During wakefulness, we found that pre-IED SOs within the IZ exhibited higher amplitudes for propagating IEDs than non-propagating IEDs (delta band: p = 0.001, theta band: p < 0.001). This increase in SOs was also concurrently observed in the NOZ (delta band: p = 0.04). Similarly, the inter-channel phase synchronization of SOs prior to propagating IEDs was higher than those preceding non-propagating IEDs in the IZ (delta band: p = 0.04). Through sliding window analysis, we observed that SOs preceding propagating IEDs progressively increased in amplitude and phase synchronization, while those preceding non-propagating IEDs remained relatively stable. Significant differences in amplitude occurred approximately 1150 ms before IEDs. During non-rapid eye movement (NREM) sleep, SOs on scalp recordings also showed higher amplitudes before intracranial propagating IEDs than before non-propagating IEDs (delta band: p = 0.006). Furthermore, the analysis of IED density around sleep SOs revealed that only high-amplitude sleep SOs demonstrated correlation with IED propagation. Overall, our study highlights that transient but widely distributed SOs are associated with IED propagation as well as generation in focal epilepsy during sleep and wakefulness, providing new insight into the EEG substrate supporting IED networks.
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Affiliation(s)
- Hongyi Ye
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Nanhu Brain-computer Interface Institute, Hangzhou, China
| | - Lingqi Ye
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lingli Hu
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuyu Yang
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yi Ge
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ruotong Chen
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shan Wang
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bo Jin
- Department of Neurology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenjie Ming
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongjin Wang
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Sha Xu
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Wang
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yao Ding
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Junming Zhu
- Department of Neurosurgery and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Meiping Ding
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhong Chen
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuang Wang
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Nanhu Brain-computer Interface Institute, Hangzhou, China.
| | - Cong Chen
- Department of Neurology and Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Zhou DJ, Gumenyuk V, Taraschenko O, Grobelny BT, Stufflebeam SM, Peled N. Visualization of the Spatiotemporal Propagation of Interictal Spikes in Temporal Lobe Epilepsy: A MEG Pilot Study. Brain Topogr 2024; 37:116-125. [PMID: 37966675 DOI: 10.1007/s10548-023-01017-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 10/25/2023] [Indexed: 11/16/2023]
Abstract
Magnetoencephalography (MEG) is clinically used to localize interictal spikes in discrete brain areas of epilepsy patients through the equivalent current dipole (ECD) method, but does not account for the temporal dynamics of spike activity. Recent studies found that interictal spike propagation beyond the temporal lobe may be associated with worse postsurgical outcomes, but studies using whole-brain data such as in MEG remain limited. In this pilot study, we developed a tool that visualizes the spatiotemporal dynamics of interictal MEG spikes normalized to spike-free sleep activity to assess their onset and propagation patterns in patients with temporal lobe epilepsy (TLE). We extracted interictal source data containing focal epileptiform activity in awake and asleep states from seven patients whose MEG ECD clusters localized to the temporal lobe and normalized the data against spike-free sleep recordings. We calculated the normalized activity over time per cortical label, confirmed maximal activity at onset, and mapped the activity over a 10 ms interval onto each patient's brain using a custom-built Multi-Modal Visualization Tool. The onset of activity in all patients appeared near the clinically determined epileptogenic zone. By 10 ms, four of the patients had propagated source activity restricted to within the temporal lobe, and three had propagated source activity spread to extratemporal regions. Using this tool, we show that noninvasively identifying the onset and propagation of interictal spike activity in MEG can be achieved, which may help provide further insight into epileptic networks and guide surgical planning and interventions in patients with TLE.
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Affiliation(s)
- Daniel J Zhou
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Valentina Gumenyuk
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Olga Taraschenko
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Bartosz T Grobelny
- Department of Neurosurgery, Saint Luke's Health System of Kansas City, Kansas City, MO, USA
| | - Steven M Stufflebeam
- MGH/HST Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Harvard Medical School, Cambridge, MA, USA
| | - Noam Peled
- MGH/HST Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
- Harvard Medical School, Cambridge, MA, USA.
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Conrad EC, Lucas A, Ojemann WK, Aguila CA, Mojena M, LaRocque JJ, Pattnaik AR, Gallagher R, Greenblatt A, Tranquille A, Parashos A, Gleichgerrcht E, Bonilha L, Litt B, Sinha S, Ungar L, Davis KA. Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.13.23299907. [PMID: 38168158 PMCID: PMC10760281 DOI: 10.1101/2023.12.13.23299907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data is captured during this process, but these data currently play a small role in surgical planning. Our objective was to predict the laterality of the seizure onset zone using interictal (between-seizure) intracranial EEG data in patients with temporal lobe epilepsy. We performed a retrospective cohort study (single-center study for model development; two-center study for model validation). We studied patients with temporal lobe epilepsy undergoing intracranial EEG at the University of Pennsylvania (internal cohort) and the Medical University of South Carolina (external cohort) between 2015 and 2022. We developed a logistic regression model to predict seizure onset zone laterality using interictal EEG. We compared the concordance between the model-predicted seizure onset zone laterality and the side of surgery between patients with good and poor surgical outcomes. 47 patients (30 women; ages 20-69; 20 left-sided, 10 right-sided, and 17 bilateral seizure onsets) were analyzed for model development and internal validation. 19 patients (10 women; ages 23-73; 5 left-sided, 10 right-sided, 4 bilateral) were analyzed for external validation. The internal cohort cross-validated area under the curve for a model trained using spike rates was 0.83 for a model predicting left-sided seizure onset and 0.68 for a model predicting right-sided seizure onset. Balanced accuracies in the external cohort were 79.3% and 78.9% for the left- and right-sided predictions, respectively. The predicted concordance between the laterality of the seizure onset zone and the side of surgery was higher in patients with good surgical outcome. In conclusion, interictal EEG signatures are distinct across seizure onset zone lateralities. Left-sided seizure onsets are easier to distinguish than right-sided onsets. A model trained on spike rates accurately identifies patients with left-sided seizure onset zones and predicts surgical outcome.
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Affiliation(s)
- Erin C. Conrad
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alfredo Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - William K.S. Ojemann
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Carlos A. Aguila
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marissa Mojena
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joshua J. LaRocque
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Akash R. Pattnaik
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ryan Gallagher
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Greenblatt
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Ashley Tranquille
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alexandra Parashos
- Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA
| | | | - Leonardo Bonilha
- Department of Neurology, Emory University, Atlanta, GA 30325, USA
| | - Brian Litt
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Saurabh Sinha
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lyle Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A. Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
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9
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Withers CP, Diamond JM, Yang B, Snyder K, Abdollahi S, Sarlls J, Chapeton JI, Theodore WH, Zaghloul KA, Inati SK. Identifying sources of human interictal discharges with travelling wave and white matter propagation. Brain 2023; 146:5168-5181. [PMID: 37527460 PMCID: PMC11046055 DOI: 10.1093/brain/awad259] [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: 03/17/2023] [Revised: 06/30/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023] Open
Abstract
Interictal epileptiform discharges have been shown to propagate from focal epileptogenic sources as travelling waves or through more rapid white matter conduction. We hypothesize that both modes of propagation are necessary to explain interictal discharge timing delays. We propose a method that, for the first time, incorporates both propagation modes to identify unique potential sources of interictal activity. We retrospectively analysed 38 focal epilepsy patients who underwent intracranial EEG recordings and diffusion-weighted imaging for epilepsy surgery evaluation. Interictal discharges were detected and localized to the most likely source based on relative delays in time of arrival across electrodes, incorporating travelling waves and white matter propagation. We assessed the influence of white matter propagation on distance of spread, timing and clinical interpretation of interictal activity. To evaluate accuracy, we compared our source localization results to earliest spiking regions to predict seizure outcomes. White matter propagation helps to explain the timing delays observed in interictal discharge sequences, underlying rapid and distant propagation. Sources identified based on differences in time of receipt of interictal discharges are often distinct from the leading electrode location. Receipt of activity propagating rapidly via white matter can occur earlier than more local activity propagating via slower cortical travelling waves. In our cohort, our source localization approach was more accurate in predicting seizure outcomes than the leading electrode location. Inclusion of white matter in addition to travelling wave propagation in our model of discharge spread did not improve overall accuracy but allowed for identification of unique and at times distant potential sources of activity, particularly in patients with persistent postoperative seizures. Since distant white matter propagation can occur more rapidly than local travelling wave propagation, combined modes of propagation within an interictal discharge sequence can decouple the commonly assumed relationship between spike timing and distance from the source. Our findings thus highlight the clinical importance of recognizing the presence of dual modes of propagation during interictal discharges, as this may be a cause of clinical mislocalization.
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Affiliation(s)
- C Price Withers
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Joshua M Diamond
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Braden Yang
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kathryn Snyder
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Shervin Abdollahi
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Joelle Sarlls
- NIH MRI Research Facility, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Julio I Chapeton
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - William H Theodore
- Clinical Epilepsy Section, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sara K Inati
- Neurophysiology of Epilepsy Unit, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
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10
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Ramantani G, Westover MB, Gliske S, Sarnthein J, Sarma S, Wang Y, Baud MO, Stacey WC, Conrad EC. Passive and active markers of cortical excitability in epilepsy. Epilepsia 2023; 64 Suppl 3:S25-S36. [PMID: 36897228 PMCID: PMC10512778 DOI: 10.1111/epi.17578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/07/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.
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Affiliation(s)
- Georgia Ramantani
- Department of Neuropediatrics and Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Data Science, Massachusetts General Hospital McCance Center for Brain Health, Boston, Massachusetts, USA
- Research Affiliate Faculty, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Research Affiliate Faculty, Broad Institute, Cambridge, Massachusetts, USA
| | - Stephen Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Sridevi Sarma
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle Upon Tyne, UK
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center, NeuroTec, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - William C Stacey
- Department of Neurology, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Division of Neurology, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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11
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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: 0] [Impact Index Per Article: 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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12
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Mohammed AH, Cabrerizo M, Pinzon A, Yaylali I, Jayakar P, Adjouadi M. Graph neural networks in EEG spike detection. Artif Intell Med 2023; 145:102663. [PMID: 37925203 DOI: 10.1016/j.artmed.2023.102663] [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: 10/12/2022] [Revised: 06/06/2023] [Accepted: 09/14/2023] [Indexed: 11/06/2023]
Abstract
OBJECTIVE This study develops new machine learning architectures that are more adept at detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results using the average precision (AP) metric is made with the proposed models on two datasets obtained from Baptist Hospital of Miami and Temple University Hospital. METHODS Applying graph neural networks (GNNs) on functional connectivity (FC) maps of different frequency sub-bands to yield a novel architecture we call FC-GNN. Attention mechanism is applied on a complete graph to let the neural network select its important edges, hence bypassing the extraction of features, a model we refer to as CA-GNN. RESULTS On the Baptist Hospital dataset, the results were as follows: Vanilla Self-Attention →0.9029±0.0431, Hierarchical Attention →0.8546±0.0587, Vanilla Visual Geometry Group (VGG) →0.92±0.0618, Satelight →0.9219±0.046, FC-GNN →0.9731±0.0187, and CA-GNN →0.9788±0.0125. In the same order, the results on the Temple University Hospital dataset are 0.9692, 0.9113, 0.97, 0.9575, 0.963, and 0.9879. CONCLUSION Based on the good results they yield, GNNs prove to have a strong potential in detecting epileptogenic activity. SIGNIFICANCE This study opens the door for the discovery of the powerful role played by GNNs in capturing IEDs, which is an essential step for identifying the epileptogenic networks of the affected brain and hence improving the prospects for more accurate 3D source localization.
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Affiliation(s)
- Ahmed Hossam Mohammed
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA.
| | - Mercedes Cabrerizo
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA
| | - Alberto Pinzon
- Epilepsy Center, Baptist Hospital of Miami, 9090 SW 87th Ct Suite201, Miami, 33176, FL, USA
| | - Ilker Yaylali
- Department of Neurology, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, 97239, OR, USA
| | - Prasanna Jayakar
- Brain Institute, Nicklaus Children's Hospital, 3100 SW 62nd Ave, Miami, FL 33155, USA
| | - Malek Adjouadi
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA
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13
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Shamas M, Yeh HJ, Fried I, Engel J, Staba RJ. High-rate leading spikes in propagating spike sequences predict seizure outcome in surgical patients with temporal lobe epilepsy. Brain Commun 2023; 5:fcad289. [PMID: 37953846 PMCID: PMC10636565 DOI: 10.1093/braincomms/fcad289] [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: 04/10/2023] [Revised: 08/14/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023] Open
Abstract
Inter-ictal spikes aid in the diagnosis of epilepsy and in planning surgery of medication-resistant epilepsy. However, the localizing information from spikes can be unreliable because spikes can propagate, and the burden of spikes, often assessed as a rate, does not always correlate with the seizure onset zone or seizure outcome. Recent work indicates identifying where spikes regularly emerge and spread could localize the seizure network. Thus, the current study sought to better understand where and how rates of single and coupled spikes, and especially brain regions with high-rate and leading spike of a propagating sequence, informs the extent of the seizure network. In 37 patients with medication-resistant temporal lobe seizures, who had surgery to treat their seizure disorder, an algorithm detected spikes in the pre-surgical depth inter-ictal EEG. A separate algorithm detected spike propagation sequences and identified the location of leading and downstream spikes in each sequence. We analysed the rate and power of single spikes on each electrode and coupled spikes between pairs of electrodes, and the proportion of sites with high-rate, leading spikes in relation to the seizure onset zone of patients seizure free (n = 19) and those with continuing seizures (n = 18). We found increased rates of single spikes in mesial temporal seizure onset zone (ANOVA, P < 0.001, η2 = 0.138), and increased rates of coupled spikes within, but not between, mesial-, lateral- and extra-temporal seizure onset zone of patients with continuing seizures (P < 0.001; η2 = 0.195, 0.113 and 0.102, respectively). In these same patients, there was a higher proportion of brain regions with high-rate leaders, and each sequence contained a greater number of spikes that propagated with a higher efficiency over a longer distance outside the seizure onset zone than patients seizure free (Wilcoxon, P = 0.0172). The proportion of high-rate leaders in and outside the seizure onset zone could predict seizure outcome with area under curve = 0.699, but not rates of single or coupled spikes (0.514 and 0.566). Rates of coupled spikes to a greater extent than single spikes localize the seizure onset zone and provide evidence for inter-ictal functional segregation, which could be an adaptation to avert seizures. Spike rates, however, have little value in predicting seizure outcome. High-rate spike sites leading propagation could represent sources of spikes that are important components of an efficient seizure network beyond the clinical seizure onset zone, and like the seizure onset zone these, too, need to be removed, disconnected or stimulated to increase the likelihood for seizure control.
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Affiliation(s)
- Mohamad Shamas
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Hsiang J Yeh
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Itzhak Fried
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Jerome Engel
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Richard J Staba
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
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14
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Li Z, Zhang H, Niu S, Xing Y. Localizing epileptogenic zones with high-frequency oscillations and directed connectivity. Seizure 2023; 111:9-16. [PMID: 37487273 DOI: 10.1016/j.seizure.2023.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/26/2023] Open
Abstract
PURPOSE Precise localization of the epileptogenic zone (EZ) is essential for epilepsy surgery. Existing methods often fail to detect slow onset patterns or similar neural activities presented in the recorded signals. To address this issue, we propose a new measure to quantify epileptogenicity, i.e., the connectivity high-frequency epileptogenicity index (cHFEI). METHODS The cHFEI method combines directed connectivity and high-frequency oscillations (HFOs) to measure the epileptogenicity of regions involved in a brain network. By applying this method to stereoelectroencephalography (SEEG) recordings of 49 seizures in 20 patients, we calculated the accuracy, sensitivity, and precision with a visually identified epileptogenic zone as a reference. The performance was evaluated by the confusion matrix and the area under the receiver operating characteristic (ROC) curve. RESULTS Epileptic network estimation based on cHFEI successfully distinguished brain regions involved in seizure onset from the propagation network. Moreover, cHFEI outperformed other existing detection methods in the estimation of EZs in all patients, with an average area under the ROC curve of 0.88 and an accuracy of 0.85. CONCLUSIONS cHFEI can characterize EZ in a robust manner despite various seizure onset patterns and has potential application in epilepsy therapy.
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Affiliation(s)
- Zhaohui Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of information transmission and signal processing, Yanshan University, Qinhuangdao 066004, China.
| | - Hao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Shipeng Niu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Yanyu Xing
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
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15
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Krestel H, Schreier DR, Sakiri E, von Allmen A, Abukhadra Y, Nirkko A, Steinlin M, Rosenow F, Markhus R, Schneider G, Jagella C, Mathis J, Blumenfeld H. Predictive Power of Interictal Epileptiform Discharges in Fitness-to-Drive Evaluation. Neurology 2023; 101:e866-e878. [PMID: 37414567 PMCID: PMC10501101 DOI: 10.1212/wnl.0000000000207531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 05/04/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND AND OBJECTIVES This study aimed to evaluate and predict the effects of interictal epileptiform discharges (IEDs) on driving ability using simple reaction tests and a driving simulator. METHODS Patients with various epilepsies were evaluated with simultaneous EEGs during their response to visual stimuli in a single-flash test, a car-driving video game, and a realistic driving simulator. Reaction times (RTs) and missed reactions or crashes (miss/crash) during normal EEG and IEDs were measured. IEDs, as considered in this study, were a series of epileptiform potentials (>1 potential) and were classified as generalized typical, generalized atypical, or focal. RT and miss/crash in relation to IED type, duration, and test type were analyzed. RT prolongation, miss/crash probability, and odds ratio (OR) of miss/crash due to IEDs were calculated. RESULTS Generalized typical IEDs prolonged RT by 164 ms, compared with generalized atypical IEDs (77.0 ms) and focal IEDs (48.0 ms) (p < 0.01). Generalized typical IEDs had a session miss/crash probability of 14.7% compared with a zero median for focal and generalized atypical IEDs (p < 0.01). Long repetitive bursts of focal IEDs lasting >2 seconds had a 2.6% miss/crash probabilityIED. Cumulated miss/crash probability could be predicted from RT prolongation: 90.3 ms yielded a 20% miss/crash probability. All tests were nonsuperior to each other in detecting miss/crash probabilitiesIED (zero median for all 3 tests) or RT prolongations (flash test: 56.4 ms, car-driving video game: 75.5 ms, simulator 86.6 ms). IEDs increased the OR of miss/crash in the simulator by 4.9-fold compared with normal EEG. A table of expected RT prolongations and miss/crash probabilities for IEDs of a given type and duration was created. DISCUSSION IED-associated miss/crash probability and RT prolongation were comparably well detected by all tests. Long focal IED bursts carry a low risk, while generalized typical IEDs are the primary cause of miss/crash. We propose a cumulative 20% miss/crash risk at an RT prolongation of 90.3 ms as a clinically relevant IED effect. The IED-associated OR in the simulator approximates the effects of sleepiness or low blood alcohol level while driving on real roads. A decision aid for fitness-to-drive evaluation was created by providing the expected RT prolongations and misses/crashes when IEDs of a certain type and duration are detected in routine EEG.
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Affiliation(s)
- Heinz Krestel
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway.
| | - David R Schreier
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
| | - Elmaze Sakiri
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
| | - Andreas von Allmen
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
| | - Yasmina Abukhadra
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
| | - Arto Nirkko
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
| | - Maja Steinlin
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
| | - Felix Rosenow
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
| | - Rune Markhus
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
| | - Gaby Schneider
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
| | - Caroline Jagella
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
| | - Johannes Mathis
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
| | - Hal Blumenfeld
- From the Departments of Neurology (H.K., Y.A., C.J., H.B.), Neuroscience (H.B.), and Neurosurgery (H.B.), Yale School of Medicine, New Haven, CT; Epilepsy Center Frankfurt Rhine-Main (H.K., F.R.), University Hospital Frankfurt, Center for Personalized Translational Epilepsy Research (CePTER), and Institute of Mathematics (G.S.), Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany; Department of Neurology (D.R.S., A.N., J.M.); Departments of Cardiology (E.S.) and Pediatric Neurology (M.S.), Bern University Hospital and University of Bern; Neurocenter Lucerne (A.N.), Switzerland; and National Centre for Epilepsy (R.M.), Division of Clinical Neuroscience Oslo University Hospital, Norway
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Pattnaik AR, Ghosn NJ, Ong IZ, Revell AY, Ojemann WKS, Scheid BH, Georgostathi G, Bernabei JM, Conrad EC, Sinha SR, Davis KA, Sinha N, Litt B. The seizure severity score: a quantitative tool for comparing seizures and their response to therapy. J Neural Eng 2023; 20:046026. [PMID: 37531949 DOI: 10.1088/1741-2552/aceca1] [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: 03/14/2023] [Accepted: 08/01/2023] [Indexed: 08/04/2023]
Abstract
Objective.Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates electroencephalography (EEG) and clinical data and demonstrate how it can guide epilepsy therapies.Approach.We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and preictal network features.Main results.The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p= 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical severity was associated with better surgical outcome (p= 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity.Significance.We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool's utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.
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Affiliation(s)
- Akash R Pattnaik
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Nina J Ghosn
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Ian Z Ong
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Andrew Y Revell
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - William K S Ojemann
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Brittany H Scheid
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Georgia Georgostathi
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - John M Bernabei
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Saurabh R Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States of America
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Weiss SA, Fried I, Engel J, Bragin A, Wang S, Sperling MR, Wong RK, Nir Y, Staba RJ. Pathological neurons generate ripples at the UP-DOWN transition disrupting information transfer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.01.23293365. [PMID: 37609251 PMCID: PMC10441494 DOI: 10.1101/2023.08.01.23293365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Objective To confirm and investigate why pathological HFOs (pHFOs), including Ripples [80-200 Hz] and fast ripples [200-600 Hz], are generated during the UP-DOWN transition of the slow wave and if pHFOs interfere with information transmission. Methods We isolated 217 total units from 175.95 iEEG contact-hours of synchronized macro- and microelectrode recordings from 6 patients. Sleep slow oscillation (0.1-2 Hz) epochs were identified in the iEEG recording. iEEG HFOs that occurred superimposed on the slow wave were transformed to phasors and adjusted by the phase of maximum firing in nearby units (i.e., maximum UP). We tested whether, in the seizure onset zone (SOZ), HFOs and associated action potentials (AP) occur more often at the UP-DOWN transition. We also examined ripple temporal correlations using cross correlograms. Results At the group level in the SOZ, HFO and HFO-associated AP probability was highest during the UP-DOWN transition of slow wave excitability (p<<0.001). In the non-SOZ, HFO and HFO-associated AP was highest during the DOWN-UP transition (p<<0.001). At the unit level in the SOZ, 15.6% and 20% of units exhibited more robust firing during ripples (Cohen's d=0.11-0.83) and fast ripples (d=0.36-0.90) at the UP-DOWN transition (p<0.05 f.d.r corrected), respectively. By comparison, also in the SOZ, 6.6% (d=0.14-0.30) and 8.5% (d=0.33-0.41) of units had significantly less firing during ripples and fast ripples at the UP-DOWN transition, respectively. Additional data shows ripple temporal correlations, involving global slow waves, between the hippocampus, entorhinal cortex, and parahippocampal gyrus were reduced by ~50-80% in the SOZ compared to the non-SOZ (N=3). Significance The UP-DOWN transition of slow wave excitability facilitates the activation of pathological neurons to generate pHFOs. The pathological neurons and pHFOs disrupt ripple temporal correlations across brain regions that transfer information and may be important in memory consolidation.
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Affiliation(s)
- Shennan A Weiss
- Dept. of Neurology
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, USA
| | | | - Jerome Engel
- Dept. of Neurology
- Dept. of Neurosurgery
- Dept. of Neurobiology
- Dept. of Psychiatry and Biobehavioral Sciences
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | | | - Shuang Wang
- Depts of Neurology, Epilepsy Center, Second Affiliated Hospital of Medical College, Zhejiang University, Zhejiang, China
| | - Michael R. Sperling
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Robert K.S. Wong
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
| | - Yuval Nir
- Department of Physiology and Pharmacology, Sackler School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
- The Sieratzki-Sagol Center for Sleep Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel
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Dellavale D, Bonini F, Pizzo F, Makhalova J, Wendling F, Badier JM, Bartolomei F, Bénar CG. Spontaneous fast-ultradian dynamics of polymorphic interictal events in drug-resistant focal epilepsy. Epilepsia 2023; 64:2027-2043. [PMID: 37199673 DOI: 10.1111/epi.17655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/16/2023] [Accepted: 05/16/2023] [Indexed: 05/19/2023]
Abstract
OBJECTIVE We studied the rate dynamics of interictal events occurring over fast-ultradian time scales, as commonly examined in clinics to guide surgical planning in epilepsy. METHODS Stereo-electroencephalography (SEEG) traces of 35 patients with good surgical outcome (Engel I) were analyzed. For this we developed a general data mining method aimed at clustering the plethora of transient waveform shapes including interictal epileptiform discharges (IEDs) and assessed the temporal fluctuations in the capability of mapping the epileptogenic zone (EZ) of each type of event. RESULTS We found that the fast-ultradian dynamics of the IED rate may effectively impair the precision of EZ identification, and appear to occur spontaneously, that is, not triggered by or exclusively associated with a particular cognitive task, wakefulness, sleep, seizure occurrence, post-ictal state, or antiepileptic drug withdrawal. Propagation of IEDs from the EZ to the propagation zone (PZ) could explain the observed fast-ultradian fluctuations in a reduced fraction of the analyzed patients, suggesting that other factors like the excitability of the epileptogenic tissue could play a more relevant role. A novel link was found between the fast-ultradian dynamics of the overall rate of polymorphic events and the rate of specific IEDs subtypes. We exploited this feature to estimate in each patient the 5 min interictal epoch for near-optimal EZ and resected-zone (RZ) localization. This approach produces at the population level a better EZ/RZ classification when compared to both (1) the whole time series available in each patient (p = .084 for EZ, p < .001 for RZ, Wilcoxon signed-rank test) and (2) 5 min epochs sampled randomly from the interictal recordings of each patient (p < .05 for EZ, p < .001 for RZ, 105 random samplings). SIGNIFICANCE Our results highlight the relevance of the fast-ultradian IED dynamics in mapping the EZ, and show how this dynamics can be estimated prospectively to inform surgical planning in epilepsy.
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Affiliation(s)
- Damián Dellavale
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Centro Atómico Bariloche and Instituto Balseiro, Comisión Nacional de Energía Atómica (CNEA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Cuyo (UNCUYO), Río Negro, San Carlos de Bariloche, Argentina
| | - Francesca Bonini
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | - Francesca Pizzo
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | - Julia Makhalova
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | | | - Jean-Michel Badier
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
| | - Fabrice Bartolomei
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
- Epileptology and Cerebral Rhythmology, APHM, Timone Hospital, Marseille, France
| | - Christian-George Bénar
- Institut de Neurosciences des Systèmes (INS, UMR1106), Aix Marseille Université, INSERM, Marseille, France
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Wang Y, Schroeder GM, Horsley JJ, Panagiotopoulou M, Chowdhury FA, Diehl B, Duncan JS, McEvoy AW, Miserocchi A, de Tisi J, Taylor PN. Temporal stability of intracranial electroencephalographic abnormality maps for localizing epileptogenic tissue. Epilepsia 2023; 64:2070-2080. [PMID: 37226553 PMCID: PMC10962550 DOI: 10.1111/epi.17663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 05/26/2023]
Abstract
OBJECTIVE Identifying abnormalities on interictal intracranial electroencephalogram (iEEG), by comparing patient data to a normative map, has shown promise for the localization of epileptogenic tissue and prediction of outcome. The approach typically uses short interictal segments of approximately 1 min. However, the temporal stability of findings has not been established. METHODS Here, we generated a normative map of iEEG in nonpathological brain tissue from 249 patients. We computed regional band power abnormalities in a separate cohort of 39 patients for the duration of their monitoring period (.92-8.62 days of iEEG data, mean = 4.58 days per patient, >4800 hours recording). To assess the localizing value of band power abnormality, we computedD RS -a measure of how different the surgically resected and spared tissue was in terms of band power abnormalities-over time. RESULTS In each patient, theD RS value was relatively consistent over time. The medianD RS of the entire recording period separated seizure-free (International League Against Epilepsy [ILAE] = 1) and not-seizure-free (ILAE> 1) patients well (area under the curve [AUC] = .69). This effect was similar interictally (AUC = .69) and peri-ictally (AUC = .71). SIGNIFICANCE Our results suggest that band power abnormality D_RS, as a predictor of outcomes from epilepsy surgery, is a relatively robust metric over time. These findings add further support for abnormality mapping of neurophysiology data during presurgical evaluation.
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Affiliation(s)
- Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
- Faculty of Medical SciencesNewcastle UniversityNewcastle Upon TyneUK
- UCL Queen Square Institute of NeurologyQueen SquareLondonUK
| | - Gabrielle M. Schroeder
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Jonathan J. Horsley
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Mariella Panagiotopoulou
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | | | - Beate Diehl
- UCL Queen Square Institute of NeurologyQueen SquareLondonUK
| | - John S. Duncan
- UCL Queen Square Institute of NeurologyQueen SquareLondonUK
| | | | | | - Jane de Tisi
- UCL Queen Square Institute of NeurologyQueen SquareLondonUK
| | - Peter N. Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of ComputingNewcastle UniversityNewcastle Upon TyneUK
- Faculty of Medical SciencesNewcastle UniversityNewcastle Upon TyneUK
- UCL Queen Square Institute of NeurologyQueen SquareLondonUK
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Gallagher RS, Sinha N, Pattnaik AR, Ojemann WK, Lucas A, LaRocque JJ, Bernabei JM, Greenblatt AS, Sweeney EM, Chen HI, Davis KA, Conrad EC, Litt B. Quantifying interictal intracranial EEG to predict focal epilepsy. ARXIV 2023:arXiv:2307.15170v1. [PMID: 37547655 PMCID: PMC10402195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Introduction Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials. Methods We analyzed interictal data from 101 patients with drug resistant epilepsy who underwent presurgical evaluation with IEEG. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. 65 patients had unifocal seizure onset on IEEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal IEEG abnormalities for each patient. We compared these measures against the "5 Sense Score (5SS)," a pre-implant estimate of the likelihood of focal seizure onset, and assessed their ability to predict the clinicians' choice of therapeutic intervention and the patient outcome. Results The spatial dispersion of IEEG electrodes predicted network focality with precision similar to the 5SS (AUC = 0.67), indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5SS and the spatial dispersion of interictal IEEG abnormalities significantly improved this prediction (AUC = 0.79; p<0.05). The combined model predicted ultimate treatment strategy (surgery vs. device) with an AUC of 0.81 and post-surgical outcome at 2 years with an AUC of 0.70. The 5SS, interictal IEEG, and electrode placement were not correlated and provided complementary information. Conclusions Quantitative, interictal IEEG significantly improved upon pre-implant estimates of network focality and predicted treatment with precision approaching that of clinical experts. We present this study as an important step in building standardized, quantitative tools to guide epilepsy surgery.
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Affiliation(s)
- Ryan S Gallagher
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Perelman School of Medicine, University of Pennsylvania
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Akash R. Pattnaik
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - William K.S. Ojemann
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - Alfredo Lucas
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Perelman School of Medicine, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | - Joshua J. LaRocque
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - John M Bernabei
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Perelman School of Medicine, University of Pennsylvania
- Department of Bioengineering, University of Pennsylvania
| | | | - Elizabeth M Sweeney
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - H Isaac Chen
- Department of Neurosurgery, University of Pennsylvania
- Corporal Michael J. Crescenz Veterans Affairs Medical Center
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Brian Litt
- Center for Neuroengineering and Therapeutics, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
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21
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Janca R, Jezdik P, Ebel M, Kalina A, Kudr M, Jahodova A, Krysl D, Mackova K, Straka B, Marusic P, Krsek P. Distinct patterns of interictal intracranial EEG in focal cortical dysplasia type I and II. Clin Neurophysiol 2023; 151:10-17. [PMID: 37121217 DOI: 10.1016/j.clinph.2023.03.360] [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: 12/28/2022] [Revised: 03/13/2023] [Accepted: 03/21/2023] [Indexed: 05/02/2023]
Abstract
OBJECTIVE Focal cortical dysplasia (FCD) is the most common malformation causing refractory focal epilepsy. Surgical removal of the entire dysplastic cortex is crucial for achieving a seizure-free outcome. Precise presurgical distinctions between FCD types by neuroimaging are difficult, mainly in patients with normal magnetic resonance imaging findings. However, the FCD type is important for planning the extent of surgical approach and counselling. METHODS This study included patients with focal drug-resistant epilepsy and definite histopathological FCD type I or II diagnoses who underwent intracranial electroencephalography (iEEG). We detected interictal epileptiform discharges (IEDs) and their recruitment into repetitive discharges (RDs) to compare electrophysiological patterns characterizing FCD types. RESULTS Patients with FCD type II had a significantly higher IED rate (p < 0.005), a shorter inter-discharge interval within RD episodes (p < 0.003), sleep influence on decreased RD periodicity (p < 0.036), and longer RD episode duration (p < 0.003) than patients with type I. A Bayesian classifier stratified FCD types with 82% accuracy. CONCLUSION Temporal characteristics of IEDs and RDs reflect the histological findings of FCD subtypes and can differentiate FCD types I and II. SIGNIFICANCE Presurgical prediction of FCD type can help to plan a more tailored surgical approach in patients with normal magnetic resonance findings.
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Affiliation(s)
- Radek Janca
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic.
| | - Petr Jezdik
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic
| | - Matyas Ebel
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Adam Kalina
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Martin Kudr
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Alena Jahodova
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - David Krysl
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Katerina Mackova
- Faculty of Electrical Engineering, Department of Circuit Theory, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic
| | - Barbora Straka
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
| | - Petr Marusic
- Department of Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006 Prague, Czech Republic(2)
| | - Pavel Krsek
- Department of Paediatric Neurology, Motol Epilepsy Center, Second Faculty of Medicine, Charles University and Motol University Hospital, V Uvalu 84, 15006, Prague, Czech Republic(2)
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Barth KJ, Sun J, Chiang CH, Qiao S, Wang C, Rahimpour S, Trumpis M, Duraivel S, Dubey A, Wingel KE, Voinas AE, Ferrentino B, Doyle W, Southwell DG, Haglund MM, Vestal M, Harward SC, Solzbacher F, Devore S, Devinsky O, Friedman D, Pesaran B, Sinha SR, Cogan GB, Blanco J, Viventi J. Flexible, high-resolution cortical arrays with large coverage capture microscale high-frequency oscillations in patients with epilepsy. Epilepsia 2023; 64:1910-1924. [PMID: 37150937 PMCID: PMC10524535 DOI: 10.1111/epi.17642] [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: 11/04/2022] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
OBJECTIVE Effective surgical treatment of drug-resistant epilepsy depends on accurate localization of the epileptogenic zone (EZ). High-frequency oscillations (HFOs) are potential biomarkers of the EZ. Previous research has shown that HFOs often occur within submillimeter areas of brain tissue and that the coarse spatial sampling of clinical intracranial electrode arrays may limit the accurate capture of HFO activity. In this study, we sought to characterize microscale HFO activity captured on thin, flexible microelectrocorticographic (μECoG) arrays, which provide high spatial resolution over large cortical surface areas. METHODS We used novel liquid crystal polymer thin-film μECoG arrays (.76-1.72-mm intercontact spacing) to capture HFOs in eight intraoperative recordings from seven patients with epilepsy. We identified ripple (80-250 Hz) and fast ripple (250-600 Hz) HFOs using a common energy thresholding detection algorithm along with two stages of artifact rejection. We visualized microscale subregions of HFO activity using spatial maps of HFO rate, signal-to-noise ratio, and mean peak frequency. We quantified the spatial extent of HFO events by measuring covariance between detected HFOs and surrounding activity. We also compared HFO detection rates on microcontacts to simulated macrocontacts by spatially averaging data. RESULTS We found visually delineable subregions of elevated HFO activity within each μECoG recording. Forty-seven percent of HFOs occurred on single 200-μm-diameter recording contacts, with minimal high-frequency activity on surrounding contacts. Other HFO events occurred across multiple contacts simultaneously, with covarying activity most often limited to a .95-mm radius. Through spatial averaging, we estimated that macrocontacts with 2-3-mm diameter would only capture 44% of the HFOs detected in our μECoG recordings. SIGNIFICANCE These results demonstrate that thin-film microcontact surface arrays with both highresolution and large coverage accurately capture microscale HFO activity and may improve the utility of HFOs to localize the EZ for treatment of drug-resistant epilepsy.
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Affiliation(s)
- Katrina J. Barth
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - James Sun
- Center for Neural Science, New York University, New York, NY, USA
| | - Chia-Han Chiang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Shaoyu Qiao
- Center for Neural Science, New York University, New York, NY, USA
| | - Charles Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Shervin Rahimpour
- Department of Neurosurgery, Clinical Neuroscience Center, University of Utah, Salt Lake City, UT, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Michael Trumpis
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Agrita Dubey
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katie E. Wingel
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alex E. Voinas
- Center for Neural Science, New York University, New York, NY, USA
| | | | - Werner Doyle
- Department of Neurosurgery, NYU Langone Medical Center, New York City, NY, USA
| | - Derek G. Southwell
- Department of Neurobiology, Duke School of Medicine, Durham, NC, USA
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Michael M. Haglund
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Matthew Vestal
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Stephen C. Harward
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Florian Solzbacher
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA
- Department of Materials Science and Engineering, University of Utah, Salt Lake City, UT, USA
| | - Sasha Devore
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Orrin Devinsky
- Department of Neurosurgery, NYU Langone Medical Center, New York City, NY, USA
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
- Comprehensive Epilepsy Center, NYU Langone Health, New York, NY, USA
| | - Daniel Friedman
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Bijan Pesaran
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Saurabh R. Sinha
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gregory B. Cogan
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
- Duke Comprehensive Epilepsy Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
| | - Justin Blanco
- Department of Electrical and Computer Engineering, United States Naval Academy, Annapolis, MD, USA
| | - Jonathan Viventi
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Department of Neurobiology, Duke School of Medicine, Durham, NC, USA
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
- Duke Comprehensive Epilepsy Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, 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: 9] [Impact Index Per Article: 9.0] [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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24
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Ghosn NJ, Xie K, Pattnaik AR, Gugger JJ, Ellis CA, Sweeney E, Fox E, Bernabei JM, Johnson J, Boccanfuso J, Litt B, Conrad EC. A pharmacokinetic model of antiseizure medication load to guide care in the epilepsy monitoring unit. Epilepsia 2023; 64:1236-1247. [PMID: 36815252 PMCID: PMC10424095 DOI: 10.1111/epi.17558] [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: 11/28/2022] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 02/24/2023]
Abstract
OBJECTIVE Evaluating patients with drug-resistant epilepsy often requires inducing seizures by tapering antiseizure medications (ASMs) in the epilepsy monitoring unit (EMU). The relationship between ASM taper strategy, seizure timing, and severity remains unclear. In this study, we developed and validated a pharmacokinetic model of total ASM load and tested its association with seizure occurrence and severity in the EMU. METHODS We studied 80 patients who underwent intracranial electroencephalographic recording for epilepsy surgery planning. We developed a first order pharmacokinetic model of the ASMs administered in the EMU to generate a continuous metric of overall ASM load. We then related modeled ASM load to seizure likelihood and severity. We determined the association between the rate of ASM load reduction, the length of hospital stay, and the probability of having a severe seizure. Finally, we used modeled ASM load to predict oncoming seizures. RESULTS Seizures occurred in the bottom 50th percentile of sampled ASM loads across the cohort (p < .0001, Wilcoxon signed-rank test), and seizures requiring rescue therapy occurred at lower ASM loads than seizures that did not require rescue therapy (logistic regression mixed effects model, odds ratio = .27, p = .01). Greater ASM decrease early in the EMU was not associated with an increased likelihood of having a severe seizure, nor with a shorter length of stay. SIGNIFICANCE A pharmacokinetic model can accurately estimate ASM levels for patients in the EMU. Lower modeled ASM levels are associated with increased seizure likelihood and seizure severity. We show that ASM load, rather than ASM taper speed, is associated with severe seizures. ASM modeling has the potential to help optimize taper strategy to minimize severe seizures while maximizing diagnostic yield.
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Affiliation(s)
- Nina J. Ghosn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kevin Xie
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Akash R. Pattnaik
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - James J. Gugger
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Colin A. Ellis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Elizabeth Sweeney
- Penn Statistics in Imaging and Visualization Endeavor Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Emily Fox
- Department of Statistics, Stanford University, Stanford, California, USA
- Department of Computer Science, Stanford University, Stanford, California, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - John M. Bernabei
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jenaye Johnson
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jacqueline Boccanfuso
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Erin C. Conrad
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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25
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Kim SJ, Nam H, Lee SA, Koo YS. Accuracy of predicting surgical outcomes using interictal electrical source imaging in patients with MRI-negative intractable epilepsy. Clin Neurol Neurosurg 2023; 229:107740. [PMID: 37119657 DOI: 10.1016/j.clineuro.2023.107740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 04/14/2023] [Accepted: 04/21/2023] [Indexed: 05/01/2023]
Abstract
OBJECTIVE We investigated the accuracy of interictal electrical source imaging (II-ESI) in localizing the epileptogenic zone in MRI-negative epilepsy patients who underwent epilepsy surgery. We also aimed to compare II-ESI's utility with other presurgical investigations and its role in guiding intracranial electroencephalography (iEEG) planning. METHODS We retrospectively reviewed the medical records of patients with operated MRI-negative intractable epilepsy at our center between 2010 and 2016. All patients underwent video electroencephalography (EEG) monitoring, high-resolution MRI, 18 fluorodeoxyglucose positron emission tomography (FDG-PET) scans, ictal single-photon emission computed tomography (SPECT) and intracranial EEG (iEEG) monitoring. We computed II-ESI following the visual identification of interictal spikes, and outcomes were determined using Engel's classification at 6 months after surgery. RESULTS Among 21 operated MRI-negative intractable epilepsy patients, 15 had sufficient data for II-ESI analysis. Of these, nine patients (60%) showed favorable outcomes corresponding to Engle's classification I and II. The localization accuracy of II-ESI was 53%, which was not significantly different from those of FDG-PET and ictal SPECT (47% and 45%, respectively). Among the patients, iEEG did not cover the areas suggested by II-ESIs in seven cases (47%). In two of those patients (29%), the regions indicated by II-ESIs were not resected, resulting in poor surgical outcomes. CONCLUSION This study demonstrates that the localization accuracy of II-ESI was comparable to ictal SPECT and the brain FDG-PET scan. II-ESI is a simple, noninvasive method for evaluating the epileptogenic zone and guiding iEEG planning in patients with MRI-negative epilepsy.
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Affiliation(s)
- Soo Jeong Kim
- Department of Neurology, Konkuk University Medical Center, Konkuk University College of Medicine, Seoul, Republic of Korea
| | - Hyojin Nam
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang-Ahm Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yong Seo Koo
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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26
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Burgos DF, Sciaccaluga M, Worby CA, Zafra-Puerta L, Iglesias-Cabeza N, Sánchez-Martín G, Prontera P, Costa C, Serratosa JM, Sánchez MP. Epm2a R240X knock-in mice present earlier cognitive decline and more epileptic activity than Epm2a -/- mice. Neurobiol Dis 2023; 181:106119. [PMID: 37059210 DOI: 10.1016/j.nbd.2023.106119] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 04/02/2023] [Accepted: 04/05/2023] [Indexed: 04/16/2023] Open
Abstract
Lafora disease is a rare recessive form of progressive myoclonic epilepsy, usually diagnosed during adolescence. Patients present with myoclonus, neurological deterioration, and generalized tonic-clonic, myoclonic, or absence seizures. Symptoms worsen until death, usually within the first ten years of clinical onset. The primary histopathological hallmark is the formation of aberrant polyglucosan aggregates called Lafora bodies in the brain and other tissues. Lafora disease is caused by mutations in either the EPM2A gene, encoding laforin, or the EPM2B gene, coding for malin. The most frequent EPM2A mutation is R241X, which is also the most prevalent in Spain. The Epm2a-/- and Epm2b-/- mouse models of Lafora disease show neuropathological and behavioral abnormalities similar to those seen in patients, although with a milder phenotype. To obtain a more accurate animal model, we generated the Epm2aR240X knock-in mouse line with the R240X mutation in the Epm2a gene, using genetic engineering based on CRISPR-Cas9 technology. Epm2aR240X mice exhibit most of the alterations reported in patients, including the presence of LBs, neurodegeneration, neuroinflammation, interictal spikes, neuronal hyperexcitability, and cognitive decline, despite the absence of motor impairments. The Epm2aR240X knock-in mouse displays some symptoms that are more severe that those observed in the Epm2a-/- knock-out, including earlier and more pronounced memory loss, increased levels of neuroinflammation, more interictal spikes and increased neuronal hyperexcitability, symptoms that more precisely resemble those observed in patients. This new mouse model can therefore be specifically used to evaluate how new therapies affects these features with greater precision.
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Affiliation(s)
- Daniel F Burgos
- Laboratory of Neurology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid 28029, Spain; Program in Neuroscience, Autonoma de Madrid University-Cajal Institute, Madrid 28029, Spain
| | - Miriam Sciaccaluga
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia 06132, Italy; Fondazione Malattie Rare Mauro Baschirotto BIRD Onlus, Longare (VI), Italy
| | - Carolyn A Worby
- University of California at San Diego, 9500 Gilman Drive, La Jolla CA92093-0721, USA
| | - Luis Zafra-Puerta
- Laboratory of Neurology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid 28029, Spain; Program in Neuroscience, Autonoma de Madrid University-Cajal Institute, Madrid 28029, Spain; Fondazione Malattie Rare Mauro Baschirotto BIRD Onlus, Longare (VI), Italy
| | - Nerea Iglesias-Cabeza
- Laboratory of Neurology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid 28029, Spain
| | - Gema Sánchez-Martín
- Laboratory of Neurology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid 28029, Spain
| | - Paolo Prontera
- Medical Genetics Unit, S. Maria della Misericordia Hospital, Perugia 06132, Italy
| | - Cinzia Costa
- Section of Neurology, S. Maria della Misericordia Hospital, Department of Medicine and Surgery, University of Perugia, Perugia 06132, Italy
| | - José M Serratosa
- Laboratory of Neurology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid 28029, Spain
| | - Marina P Sánchez
- Laboratory of Neurology, Instituto de Investigación Sanitaria-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid 28040, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid 28029, Spain.
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27
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Thomas J, Kahane P, Abdallah C, Avigdor T, Zweiphenning WJEM, Chabardes S, Jaber K, Latreille V, Minotti L, Hall J, Dubeau F, Gotman J, Frauscher B. A Subpopulation of Spikes Predicts Successful Epilepsy Surgery Outcome. Ann Neurol 2023; 93:522-535. [PMID: 36373178 DOI: 10.1002/ana.26548] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Epileptic spikes are the traditional interictal electroencephalographic (EEG) biomarker for epilepsy. Given their low specificity for identifying the epileptogenic zone (EZ), they are given only moderate attention in presurgical evaluation. This study aims to demonstrate that it is possible to identify specific spike features in intracranial EEG that optimally define the EZ and predict surgical outcome. METHODS We analyzed spike features on stereo-EEG segments from 83 operated patients from 2 epilepsy centers (37 Engel IA) in wakefulness, non-rapid eye movement sleep, and rapid eye movement sleep. After automated spike detection, we investigated 135 spike features based on rate, morphology, propagation, and energy to determine the best feature or feature combination to discriminate the EZ in seizure-free and non-seizure-free patients by applying 4-fold cross-validation. RESULTS The rate of spikes with preceding gamma activity in wakefulness performed better for surgical outcome classification (4-fold area under receiver operating characteristics curve [AUC] = 0.755 ± 0.07) than the seizure onset zone, the current gold standard (AUC = 0.563 ± 0.05, p = 0.015) and the ripple rate, an emerging seizure-independent biomarker (AUC = 0.537 ± 0.07, p = 0.006). Channels with a spike-gamma rate exceeding 1.9/min had an 80% probability of being in the EZ. Combining features did not improve the results. INTERPRETATION Resection of brain regions with high spike-gamma rates in wakefulness is associated with a high probability of achieving seizure freedom. This rate could be applied to determine the minimal number of spiking channels requiring resection. In addition to quantitative analysis, this feature is easily accessible to visual analysis, which could aid clinicians during presurgical evaluation. ANN NEUROL 2023;93:522-535.
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Affiliation(s)
- John Thomas
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Philippe Kahane
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Chifaou Abdallah
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Tamir Avigdor
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Willemiek J E M Zweiphenning
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Stephan Chabardes
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Kassem Jaber
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Véronique Latreille
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Lorella Minotti
- Grenoble Alpes University Hospital Center, Grenoble Alpes University, Inserm, U1216, Grenoble Institute Neurosciences, Grenoble, France
| | - Jeff Hall
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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28
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Conrad EC, Revell AY, Greenblatt AS, Gallagher RS, Pattnaik AR, Hartmann N, Gugger JJ, Shinohara RT, Litt B, Marsh ED, Davis KA. Spike patterns surrounding sleep and seizures localize the seizure-onset zone in focal epilepsy. Epilepsia 2023; 64:754-768. [PMID: 36484572 PMCID: PMC10045742 DOI: 10.1111/epi.17482] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 12/08/2022] [Accepted: 12/08/2022] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Interictal spikes help localize seizure generators as part of surgical planning for drug-resistant epilepsy. However, there are often multiple spike populations whose frequencies change over time, influenced by brain state. Understanding state changes in spike rates will improve our ability to use spikes for surgical planning. Our goal was to determine the effect of sleep and seizures on interictal spikes, and to use sleep and seizure-related changes in spikes to localize the seizure-onset zone (SOZ). METHODS We performed a retrospective analysis of intracranial electroencephalography (EEG) data from patients with focal epilepsy. We automatically detected interictal spikes and we classified different time periods as awake or asleep based on the ratio of alpha to delta power, with a secondary analysis using the recently published SleepSEEG algorithm. We analyzed spike rates surrounding sleep and seizures. We developed a model to localize the SOZ using state-dependent spike rates. RESULTS We analyzed data from 101 patients (54 women, age range 16-69). The normalized alpha-delta power ratio accurately classified wake from sleep periods (area under the curve = .90). Spikes were more frequent in sleep than wakefulness and in the post-ictal compared to the pre-ictal state. Patients with temporal lobe epilepsy had a greater wake-to-sleep and pre- to post-ictal spike rate increase compared to patients with extra-temporal epilepsy. A machine-learning classifier incorporating state-dependent spike rates accurately identified the SOZ (area under the curve = .83). Spike rates tended to be higher and better localize the seizure-onset zone in non-rapid eye movement (NREM) sleep than in wake or REM sleep. SIGNIFICANCE The change in spike rates surrounding sleep and seizures differs between temporal and extra-temporal lobe epilepsy. Spikes are more frequent and better localize the SOZ in sleep, particularly in NREM sleep. Quantitative analysis of spikes may provide useful ancillary data to localize the SOZ and improve surgical planning.
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Affiliation(s)
- Erin C. Conrad
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - Andrew Y. Revell
- Medical Scientist Training Program, University of Pennsylvania, Philadelphia, PA
| | | | - Ryan S. Gallagher
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Akash R. Pattnaik
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
| | - Nicole Hartmann
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - James J. Gugger
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA
| | - Brian Litt
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
| | - Eric D. Marsh
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
- Division of Child Neurology, Department of Biostatistics, University of Pennsylvania, Epidemiology, & Informatics, Philadelphi Department of Biostatistics, University of Pennsylvania, Epidemiology, & Informatics, Philadelphi Pediatric Epilepsy Program, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Kathryn A. Davis
- Department of Neurology, University of Pennsylvania, Philadelphia, PA
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Alamoudi OA, Ilyas A, Pati S, Iasemidis L. Interictal localization of the epileptogenic zone: Utilizing the observed resonance behavior in the spectral band of surrounding inhibition. Front Neurosci 2022; 16:993678. [PMID: 36578827 PMCID: PMC9791262 DOI: 10.3389/fnins.2022.993678] [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: 07/14/2022] [Accepted: 11/16/2022] [Indexed: 12/14/2022] Open
Abstract
Introduction The gold standard for identification of the epileptogenic zone (EZ) continues to be the visual inspection of electrographic changes around seizures' onset by experienced electroencephalography (EEG) readers. Development of an epileptogenic focus localization tool that can delineate the EZ from analysis of interictal (seizure-free) periods is still an open question of great significance for improved diagnosis (e.g., presurgical evaluation) and treatment of epilepsy (e.g., surgical outcome). Methods We developed an EZ interictal localization algorithm (EZILA) based on novel analysis of intracranial EEG (iEEG) using a univariate periodogram-type power measure, a straight-forward ranking approach, a robust dimensional reduction method and a clustering technique. Ten patients with temporal and extra temporal lobe epilepsies, and matching the inclusion criteria of having iEEG recordings at the epilepsy monitoring unit (EMU) and being Engel Class I ≥12 months post-surgery, were recruited in this study. Results In a nested k-fold cross validation statistical framework, EZILA assigned the highest score to iEEG channels within the EZ in all patients (10/10) during the first hour of the iEEG recordings and up to their first typical clinical seizure in the EMU (i.e., early interictal period). To further validate EZILA's performance, data from two new (Engel Class I) patients were analyzed in a double-blinded fashion; the EZILA successfully localized iEEG channels within the EZ from interictal iEEG in both patients. Discussion Out of the sampled brain regions, iEEG channels in the EZ were most frequently and maximally active in seizure-free (interictal) periods across patients in specific narrow gamma frequency band (∼60-80 Hz), which we have termed focal frequency band (FFB). These findings are consistent with the hypothesis that the EZ may interictally be regulated (controlled) by surrounding inhibitory neurons with resonance characteristics within this narrow gamma band.
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Affiliation(s)
- Omar A. Alamoudi
- Biomedical Engineering Program, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia,Neurology Department, Texas Institute for Restorative Neurotechnologies (TIRN), University of Texas Medical School, Houston, TX, United States,*Correspondence: Omar A. Alamoudi,
| | - Adeel Ilyas
- Neurology Department, Texas Institute for Restorative Neurotechnologies (TIRN), University of Texas Medical School, Houston, TX, United States,Department of Neurological Surgery, University of Alabama at Birmingham, Birmingham, AL, United States,Vivian L. Smith Department of Neurosurgery, McGovern Medical School at University of Texas (UT) Health Houston, Houston, TX, United States
| | - Sandipan Pati
- Neurology Department, Texas Institute for Restorative Neurotechnologies (TIRN), University of Texas Medical School, Houston, TX, United States
| | - Leon Iasemidis
- Biomedical Engineering Department, Arizona State University, Tempe, AZ, United States,Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ, United States
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Gunnarsdottir KM, Li A, Smith RJ, Kang JY, Korzeniewska A, Crone NE, Rouse AG, Cheng JJ, Kinsman MJ, Landazuri P, Uysal U, Ulloa CM, Cameron N, Cajigas I, Jagid J, Kanner A, Elarjani T, Bicchi MM, Inati S, Zaghloul KA, Boerwinkle VL, Wyckoff S, Barot N, Gonzalez-Martinez J, Sarma SV. Source-sink connectivity: a novel interictal EEG marker for seizure localization. Brain 2022; 145:3901-3915. [PMID: 36412516 PMCID: PMC10200292 DOI: 10.1093/brain/awac300] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 07/05/2022] [Accepted: 08/01/2022] [Indexed: 07/26/2023] Open
Abstract
Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.
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Affiliation(s)
| | - Adam Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Rachel J Smith
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Joon-Yi Kang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Anna Korzeniewska
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Nathan E Crone
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Adam G Rouse
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Jennifer J Cheng
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Michael J Kinsman
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Patrick Landazuri
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Utku Uysal
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Carol M Ulloa
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Nathaniel Cameron
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Iahn Cajigas
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Jonathan Jagid
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Andres Kanner
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Turki Elarjani
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Manuel Melo Bicchi
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sara Inati
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Varina L Boerwinkle
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Sarah Wyckoff
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Niravkumar Barot
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | | | - Sridevi V Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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31
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Staley K. Gene therapy for epilepsy. Science 2022; 378:471-472. [DOI: 10.1126/science.ade8836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
On-demand inhibition of neuronal activity reduced spontaneous seizures in mice
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Affiliation(s)
- Kevin Staley
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Subramanian M, Chiang CC, Couturier NH, Durand DM. Theta waves, neural spikes and seizures can propagate by ephaptic coupling in vivo. Exp Neurol 2022; 354:114109. [PMID: 35551899 PMCID: PMC10214533 DOI: 10.1016/j.expneurol.2022.114109] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 04/30/2022] [Accepted: 05/05/2022] [Indexed: 11/29/2022]
Abstract
Electric field coupling has been shown to be responsible for non-synaptic neural activity propagation in hippocampal slices and cortical slices. Epileptiform and slow-wave sleep activity can propagate by electric field coupling without using synaptic connections at speeds of ~0.1 m/s in vitro. However, the characteristics of the events that can propagate using electric field coupling through a volume conductor in vivo have not been studied. Thus, we tested the hypothesis that various types of neural signals such as interictal spikes, theta waves and seizures could propagate in vivo across a transection in the hippocampus. We induced epileptiform activity in 4 rats under anesthesia by injecting 4-aminopyridine in the temporal region of the hippocampus, four recording electrodes were inserted along the longitudinal axis of the hippocampus. A transection was made between the electrodes to study the propagation of the neural activity. Although 54% of the interictal spikes could propagate through the cut, only those spikes with a high amplitude and short duration had a high probability to do so. 70% of seizure events could propagate through the cut but parameters distinguishing between propagating and non-propagating seizure events could not be identified. Theta activity was also observed to propagate at a mean speed of 0.16 ± 0.12 m/s in the characteristic range of propagation using electric field coupling through the transection. The electric field volume conduction mechanism was confirmed by showing that propagation was blocked by placing a dielectric layer within the cut. The speed of propagation was not affected by the transection thereby providing further evidence that various types of neural signals including activity in the theta range can propagate by electric field coupling in-vivo.
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Affiliation(s)
- Muthumeenakshi Subramanian
- Neural Engineering Center, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Chia-Chu Chiang
- Neural Engineering Center, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Nicholas H Couturier
- Neural Engineering Center, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Dominique M Durand
- Neural Engineering Center, Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
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Fouad A, Azizollahi H, Le Douget JE, Lejeune FX, Valderrama M, Mayor L, Navarro V, Le Van Quyen M. Interictal epileptiform discharges show distinct spatiotemporal and morphological patterns across wake and sleep. Brain Commun 2022; 4:fcac183. [PMID: 36483575 PMCID: PMC9724782 DOI: 10.1093/braincomms/fcac183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 03/24/2022] [Accepted: 07/15/2022] [Indexed: 03/19/2024] Open
Abstract
Presurgical evaluation of mesial temporal and neocortical focal pharmacoresistant epilepsy patients using intracranial EEG recordings has led to the generation of extensive data on interictal epileptiform discharges, located within or remotely from seizure onset zones. In this study, we used this data to investigate how interictal epileptiform discharges are modulated and how their spatial distribution changes during wake and sleep and analysed the relationship between these discharge events and seizure onset zones. Preoperative evaluation data from 11 adult patients with focal pharmacoresistant epilepsy were extracted from the Epilepsiae database. Interictal epileptiform discharges were automatically detected during wakefulness and over several hours of continuous seizure-free sleep (total duration of EEG recordings:106.7 h; mean per patient: 9.7 h), and analysed across four brain areas (mesial temporal, lateral neocortical, basal cortical and the temporal pole). Sleep stages were classified manually from scalp EEG. Discharge events were characterized according to their rate and morphology (amplitude, sharpness and duration). Eight patients had a seizure onset zone over mesial areas and three patients over lateral neocortical areas. Overall, discharge rates varied across brain areas during wakefulness and sleep [wake/sleep stages × brain areas interaction; Wald χ 2(df = 6) = 31.1, P < 0.0001]. N2-N3 non-rapid eye movement sleep increased interictal epileptiform discharges in mesial areas compared with wakefulness and rapid eye movement sleep (P < 0.0001), and to other areas (P < 0.0001 for all comparisons). This mesial pattern was observed both within and outside of seizure onset zones. During wakefulness, the rate of interictal epileptiform discharges was significantly higher than during N2-N3 non-rapid eye movement sleep (P = 0.04), and rapid eye movement sleep (P = 0.01) in lateral neocortical areas (referred to as lateral neocortical pattern), a finding that was more pronounced in seizures onset zones (P = 0.004). The morphological characteristics of the discharge events were modulated during wakefulness and sleep stages across brain areas. The effect of seizure onset zones on discharge morphology was conditioned by brain area and was particularly marked in temporal pole areas. Our analysis of discharge patterns in relation to cerebral localization, vigilance state and the anatomical affiliation of seizure onset zones revealed the global and local aspects of the complex relationship between interictal discharges, sleep and seizure onset zones. This novel approach may lead to a better understanding of cognitive decline and responses to therapy, as well as to adaptation of surgical interventions for epileptic patients.
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Affiliation(s)
- Amal Fouad
- Bioelectrics Lab, Paris Brain Institute (ICM Institut du Cerveau), (UMRS 1127, CNRS UMR 7225), Pitié-Salpêtriere Hospital, 75013 Paris, France
- Department of Neurology, Faculty of medicine, Ain-Shams University, Cairo, Egypt
| | - Hamed Azizollahi
- Bioelectrics Lab, Paris Brain Institute (ICM Institut du Cerveau), (UMRS 1127, CNRS UMR 7225), Pitié-Salpêtriere Hospital, 75013 Paris, France
- Bioserenity, Paris Brain Institute (ICM Institut du Cerveau), 75013 Paris, France
| | - Jean-Eudes Le Douget
- Bioelectrics Lab, Paris Brain Institute (ICM Institut du Cerveau), (UMRS 1127, CNRS UMR 7225), Pitié-Salpêtriere Hospital, 75013 Paris, France
- Bioserenity, Paris Brain Institute (ICM Institut du Cerveau), 75013 Paris, France
| | - François-Xavier Lejeune
- Sorbonne University, Paris, France
- Paris Brain Institute (ICM Institut du Cerveau), AP-HP, INSERM, CNRS, University Hospital Pitié-Salpêtrière, 75013 Paris, France
- Paris Brain Institute's Data and Analysis Core (ICM Institut du Cerveau), University Hospital Pitié-Salpêtrière, 75013 Paris, France
| | - Mario Valderrama
- Department of Biomedical Engineering, University of los Andes, Bogotá, Colombia
| | | | - Vincent Navarro
- Sorbonne University, Paris, France
- Paris Brain Institute (ICM Institut du Cerveau), AP-HP, INSERM, CNRS, University Hospital Pitié-Salpêtrière, 75013 Paris, France
- Epileptology Unit, AP-HP Pitié-Salpêtrière Hospital, 75013 Paris, France
| | - Michel Le Van Quyen
- Bioelectrics Lab, Paris Brain Institute (ICM Institut du Cerveau), (UMRS 1127, CNRS UMR 7225), Pitié-Salpêtriere Hospital, 75013 Paris, France
- Sorbonne University, Paris, France
- Laboratoire D’Imagerie Biomédicale, (INSERM U1146, UMR7371, CNRS), Sorbonne University, Paris, France
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Conrad EC, Shinohara RT, Gugger JJ, Revell AY, Das S, Stein JM, Marsh ED, Davis KA, Litt B. Implanting intracranial electrodes does not affect spikes or network connectivity in nearby or connected brain regions. Netw Neurosci 2022; 6:834-849. [PMID: 36607198 PMCID: PMC9810371 DOI: 10.1162/netn_a_00248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/01/2022] [Indexed: 01/10/2023] Open
Abstract
To determine the effect of implanting electrodes on electrographic features of nearby and connected brain regions in patients with drug-resistant epilepsy, we analyzed intracranial EEG recordings from 10 patients with drug-resistant epilepsy who underwent implant revision (placement of additional electrodes) during their hospitalization. We performed automated spike detection and measured EEG functional networks. We analyzed the original electrodes that remained in place throughout the full EEG recording, and we measured the change in spike rates and network connectivity in these original electrodes in response to implanting new electrodes. There was no change in overall spike rate pre- to post-implant revision (t(9) = 0.1, p = 0.95). The peri-revision change in the distribution of spike rate and connectivity across electrodes was no greater than chance (Monte Carlo method, spikes: p = 0.40, connectivity: p = 0.42). Electrodes closer to or more functionally connected to the revision site had no greater change in spike rate or connectivity than more distant or less connected electrodes. Changes in electrographic features surrounding electrode implantation are no greater than baseline fluctuations occurring throughout the intracranial recording. These findings argue against an implant effect on spikes or network connectivity in nearby or connected brain regions.
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Affiliation(s)
- Erin C. Conrad
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - James J. Gugger
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew Y. Revell
- Medical Scientist Training Program, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Joel M. Stein
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Eric D. Marsh
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Division of Child Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kathryn A. Davis
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Litt
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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Bernabei JM, Sinha N, Arnold TC, Conrad E, Ong I, Pattnaik AR, Stein JM, Shinohara RT, Lucas TH, Bassett DS, Davis KA, Litt B. Normative intracranial EEG maps epileptogenic tissues in focal epilepsy. Brain 2022; 145:1949-1961. [PMID: 35640886 PMCID: PMC9630716 DOI: 10.1093/brain/awab480] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 11/14/2021] [Accepted: 11/26/2021] [Indexed: 07/25/2023] Open
Abstract
Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.
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Affiliation(s)
- John M Bernabei
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Nishant Sinha
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
| | - T Campbell Arnold
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Erin Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Ian Ong
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Akash R Pattnaik
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Joel M Stein
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy H Lucas
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia PA 19104, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
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Abdallah C, Hedrich T, Koupparis A, Afnan J, Hall JA, Gotman J, Dubeau F, von Ellenrieder N, Frauscher B, Kobayashi E, Grova C. Clinical Yield of Electromagnetic Source Imaging and Hemodynamic Responses in Epilepsy: Validation With Intracerebral Data. Neurology 2022; 98:e2499-e2511. [PMID: 35473762 PMCID: PMC9231837 DOI: 10.1212/wnl.0000000000200337] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 02/21/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Accurate delineation of the seizure-onset zone (SOZ) in focal drug-resistant epilepsy often requires stereo-EEG (SEEG) recordings. Our aims were to propose a truly objective and quantitative comparison between EEG/magnetoencephalography (MEG) source imaging (EMSI), EEG/fMRI responses for similar spikes with primary irritative zone (PIZ) and SOZ defined by SEEG and to evaluate the value of EMSI and EEG/fMRI to predict postsurgical outcome. METHODS We identified patients with drug-resistant epilepsy who underwent EEG/MEG, EEG/fMRI, and subsequent SEEG at the Epilepsy Service from the Montreal Neurological Institute and Hospital. We quantified multimodal concordance within the SEEG channel space as spatial overlap with PIZ/SOZ and distances to the spike-onset, spike maximum amplitude and seizure core intracerebral channels by applying a new methodology consisting of converting EMSI results into SEEG electrical potentials (EMSIe-SEEG) and projecting the most significant fMRI response on the SEEG channels (fMRIp-SEEG). Spatial overlaps with PIZ/SOZ (AUCPIZ, AUCSOZ) were assessed by using the area under the receiver operating characteristic curve (AUC). Here, AUC represents the probability that a randomly picked active contact exhibited higher amplitude when located inside the spatial reference than outside. RESULTS Seventeen patients were included. Mean spatial overlaps with the PIZ and SOZ were 0.71 and 0.65 for EMSIe-SEEG and 0.57 and 0.62 for fMRIp-SEEG. Good EMSIe-SEEG spatial overlap with the PIZ was associated with smaller distance from the maximum EMSIe-SEEG contact to the spike maximum amplitude channel (median distance 14 mm). Conversely, good fMRIp-SEEG spatial overlap with the SOZ was associated with smaller distances from the maximum fMRIp-SEEG contact to the spike-onset and seizure core channels (median distances 10 and 5 mm, respectively). Surgical outcomes were correctly predicted by EEG/MEG in 12 of 15 (80%) patients and EEG/fMRI in 6 of 11(54%) patients. DISCUSSION With the use of a unique quantitative approach estimating EMSI and fMRI results in the reference SEEG channel space, EEG/MEG and EEG/fMRI accurately localized the SOZ and the PIZ. Precisely, EEG/MEG more accurately localized the PIZ, whereas EEG/fMRI was more sensitive to the SOZ. Both neuroimaging techniques provide complementary localization that can help guide SEEG implantation and select good candidates for surgery.
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Affiliation(s)
- Chifaou Abdallah
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada.
| | - Tanguy Hedrich
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Andreas Koupparis
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Jawata Afnan
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Jeffrey Alan Hall
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Jean Gotman
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Francois Dubeau
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Nicolas von Ellenrieder
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Eliane Kobayashi
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Christophe Grova
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
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Jiang H, Kokkinos V, Ye S, Urban A, Bagić A, Richardson M, He B. Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2200887. [PMID: 35545899 PMCID: PMC9218648 DOI: 10.1002/advs.202200887] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Indexed: 05/23/2023]
Abstract
Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug-resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. They found flatter 1/f power slope in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions. Greater differences in resting-state information flow between SOZ and non-SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.
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Affiliation(s)
- Haiteng Jiang
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
- Department of NeurobiologyAffiliated Mental Health Center & Hangzhou Seventh People's HospitalZhejiang University School of MedicineHangzhou310013P. R. China
- NHC and CAMS Key Laboratory of Medical NeurobiologyMOE Frontier Science Center for Brain Science and Brain‐machine IntegrationSchool of Brain Science and Brain MedicineZhejiang UniversityHangzhou310058P. R. China
| | - Vasileios Kokkinos
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
- Massachusetts General HospitalBostonMA02114USA
| | - Shuai Ye
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
| | - Alexandra Urban
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
| | - Anto Bagić
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
| | - Mark Richardson
- University of Pittsburgh Comprehensive Epilepsy CenterDepartment of NeurologyUniversity of Pittsburgh School of MedicinePittsburghPA15232USA
- Massachusetts General HospitalBostonMA02114USA
| | - Bin He
- Department of Biomedical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
- Neuroscience InstituteCarnegie Mellon UniversityPittsburghPA15213USA
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38
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Panagiotopoulou M, Papasavvas CA, Schroeder GM, Thomas RH, Taylor PN, Wang Y. Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions. Hum Brain Mapp 2022; 43:2460-2477. [PMID: 35119173 PMCID: PMC9057101 DOI: 10.1002/hbm.25796] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/30/2021] [Accepted: 01/23/2022] [Indexed: 01/14/2023] Open
Abstract
Epilepsy is recognised as a dynamic disease, where both seizure susceptibility and seizure characteristics themselves change over time. Specifically, we recently quantified the variable electrographic spatio-temporal seizure evolutions that exist within individual patients. This variability appears to follow subject-specific circadian, or longer, timescale modulations. It is therefore important to know whether continuously recorded interictaliEEG features can capture signatures of these modulations over different timescales. In this study, we analyse continuous intracranial electroencephalographic (iEEG) recordings from video-telemetry units and find fluctuations in iEEG band power over timescales ranging from minutes up to 12 days. As expected and in agreement with previous studies, we find that all subjects show a circadian fluctuation in their iEEG band power. We additionally detect other fluctuations of similar magnitude on subject-specific timescales. Importantly, we find that a combination of these fluctuations on different timescales can explain changes in seizure evolutions in most subjects above chance level. These results suggest that subject-specific fluctuations in iEEG band power over timescales of minutes to days may serve as markers of seizure modulating processes. We hope that future study can link these detected fluctuations to their biological driver(s). There is a critical need to better understand seizure modulating processes, as this will enable the development of novel treatment strategies that could minimise the seizure spread, duration or severity and therefore the clinical impact of seizures.
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Affiliation(s)
- Mariella Panagiotopoulou
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Christoforos A. Papasavvas
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Gabrielle M. Schroeder
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
| | - Rhys H. Thomas
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
| | - Peter N. Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
- UCL Queen Square Institute of Neurology, Queen SquareLondon
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems GroupSchool of Computing, Newcastle UniversityNewcastle upon Tyne
- Faculty of Medical SciencesNewcastle UniversityNewcastle upon Tyne
- UCL Queen Square Institute of Neurology, Queen SquareLondon
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39
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Taylor PN, Papasavvas CA, Owen TW, Schroeder GM, Hutchings FE, Chowdhury FA, Diehl B, Duncan JS, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Wang Y. Normative brain mapping of interictal intracranial EEG to localize epileptogenic tissue. Brain 2022; 145:939-949. [PMID: 35075485 PMCID: PMC9050535 DOI: 10.1093/brain/awab380] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 08/19/2021] [Accepted: 09/03/2021] [Indexed: 11/14/2022] Open
Abstract
The identification of abnormal electrographic activity is important in a wide range of neurological disorders, including epilepsy for localizing epileptogenic tissue. However, this identification may be challenging during non-seizure (interictal) periods, especially if abnormalities are subtle compared to the repertoire of possible healthy brain dynamics. Here, we investigate if such interictal abnormalities become more salient by quantitatively accounting for the range of healthy brain dynamics in a location-specific manner. To this end, we constructed a normative map of brain dynamics, in terms of relative band power, from interictal intracranial recordings from 234 participants (21 598 electrode contacts). We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions. We proposed that if the most abnormal regions were spared by surgery, then patients would be more likely to experience continued seizures postoperatively. We first confirmed that the spatial variations of band power in the normative map across brain regions were consistent with healthy variations reported in the literature. Second, when accounting for the normative variations, regions that were spared by surgery were more abnormal than those resected only in patients with persistent postoperative seizures (t = -3.6, P = 0.0003), confirming our hypothesis. Third, we found that this effect discriminated patient outcomes (area under curve 0.75 P = 0.0003). Normative mapping is a well-established practice in neuroscientific research. Our study suggests that this approach is feasible to detect interictal abnormalities in intracranial EEG, and of potential clinical value to identify pathological tissue in epilepsy. Finally, we make our normative intracranial map publicly available to facilitate future investigations in epilepsy and beyond.
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Affiliation(s)
- Peter N Taylor
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Christoforos A Papasavvas
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Thomas W Owen
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Gabrielle M Schroeder
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Frances E Hutchings
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
| | - Fahmida A Chowdhury
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Beate Diehl
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - John S Duncan
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Andrew W McEvoy
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Anna Miserocchi
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Jane de Tisi
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Sjoerd B Vos
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Matthew C Walker
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
| | - Yujiang Wang
- CNNP Laboratory (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle Helix, Newcastle University, Newcastle-upon-Tyne, NE4 5TG, UK
- UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery (NHNN), Queen Square, London WC1N 3BG, UK
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Shu S, Luo S, Cao M, Xu K, Qin L, Zheng L, Xu J, Wang X, Gao JH. Informed MEG/EEG source imaging reveals the locations of interictal spikes missed by SEEG. Neuroimage 2022; 254:119132. [PMID: 35337964 DOI: 10.1016/j.neuroimage.2022.119132] [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: 05/06/2021] [Revised: 02/18/2022] [Accepted: 03/21/2022] [Indexed: 11/19/2022] Open
Abstract
Determining the accurate locations of interictal spikes has been fundamental in the presurgical evaluation of epilepsy surgery. Stereo-electroencephalography (SEEG) is able to directly record cortical activity and localize interictal spikes. However, the main caveat of SEEG techniques is that they have limited spatial sampling (covering <5% of the whole brain), which may lead to missed spikes originating from brain regions that were not covered by SEEG. To address this problem, we propose a SEEG-informed minimum-norm estimates (SIMNE) method by combining SEEG with magnetoencephalography (MEG) or EEG. Specifically, the spike locations determined by SEEG offer as a priori information to guide MEG source reconstruction. Both computer simulations and experiments using data from five epilepsy patients were conducted to evaluate the performance of SIMNE. Our results demonstrate that SIMNE generates more accurate source estimation than a traditional minimum-norm estimates method and reveals the locations of spikes missed by SEEG, which would improve presurgical evaluation of the epileptogenic zone.
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Affiliation(s)
- Su Shu
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Shen Luo
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Miao Cao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Ke Xu
- Department of Neurosurgery, Sanbo Brain Hospital of Capital Medical University, Beijing 100093, China
| | - Lang Qin
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Li Zheng
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Jing Xu
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Laboratory of Applied Brain and Cognitive Sciences, College of International Business, Shanghai International Studies University, Shanghai 201620, China
| | - Xiongfei Wang
- Department of Neurosurgery, Sanbo Brain Hospital of Capital Medical University, Beijing 100093, China
| | - Jia-Hong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; McGovern Institute for Brain Research, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China.
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Neurophysiological Evidence for Cognitive Map Formation during Sequence Learning. eNeuro 2022; 9:ENEURO.0361-21.2022. [PMID: 35105662 PMCID: PMC8896554 DOI: 10.1523/eneuro.0361-21.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 12/03/2021] [Accepted: 01/03/2022] [Indexed: 12/29/2022] Open
Abstract
Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, few explicit links have been made between predictions from Euclidean spatial navigation and neural activity during human sequence learning. Here, we use a combination of behavioral modeling and intracranial encephalography (iEEG) recordings to investigate how neural activity might support the formation of space-like cognitive maps through temporal discounting during sequence learning. Specifically, we acquire human reaction times from a sequential reaction time task, to which we fit a model that formulates the amount of temporal discounting as a single free parameter. From the parameter, we calculate each individual's estimate of the latent space. We find that neural activity reflects these estimates mostly in the temporal lobe, including areas involved in spatial navigation. Similar to spatial navigation, we find that low-dimensional representations of neural activity allow for easy separation of important features, such as modules, in the latent space. Lastly, we take advantage of the high temporal resolution of iEEG data to determine the time scale on which latent spaces are learned. We find that learning typically happens within the first 500 trials, and is modulated by the underlying latent space and the amount of temporal discounting characteristic of each participant. Ultimately, this work provides important links between behavioral models of sequence learning and neural activity during the same behavior, and contextualizes these results within a broader framework of domain general cognitive maps.
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42
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Cui D, Gao R, Xu C, Yan H, Zhang X, Yu T, Zhang G. Ictal onset stereoelectroencephalography patterns in temporal lobe epilepsy: type, distribution, and prognostic value. Acta Neurochir (Wien) 2022; 164:555-563. [PMID: 35041086 DOI: 10.1007/s00701-022-05122-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 01/11/2022] [Indexed: 12/19/2022]
Abstract
OBJECTIVE The aim of this study was to investigate the different ictal onset stereoelectroencephalography patterns (IOPs) in patients with drug-resistant temporal lobe epilepsy (TLE). We examined whether the IOPs relate to different TLE subtypes, MRI findings, and underlying pathologies, and we evaluated their prognostic value for predicting the surgical outcome. METHODS We retrospectively analyzed data from patients with TLE who underwent stereoelectroencephalography (SEEG) monitoring followed by surgical resection between January 2018 and January 2020. The SEEG recordings were independently analyzed by two epileptologists. RESULTS Forty-five patients were included in the study, and 61seizures were analyzed. Five IOPs were identified: low voltage fast activity (LVFA; 44.3%), spike-and-wave activity (16.4%), low frequency high-amplitude periodic spikes (LFPS; 18%), a burst of high-amplitude polyspikes (8.2%), and rhythmic sharp activity at ≤ 13 Hz (13.1%). Thirty-two patients were found to have a single IOP, while the other 13 patients had two or more IOPs. All five IOPs were found to occur in the medial temporal lobe epilepsy (MTLE), while four IOPs occurred in the lateral temporal lobe epilepsy (LTLE). The LFPS was a common IOP that could distinguish MTLE from LTLE (x2 = 7.046, p = 0.011). Among the MTLE patients, the LFPS was exclusively seen in cases of hippocampal sclerosis (x2 = 5.058, p = 0.038), while the LVFA was associated with nonspecific histology (x2 = 6.077, p = 0.023). The IOPs were not found to differ according to whether the MRI scans were positive or negative. After surgery, patients achieved the higher seizure-free rate at 81.8% and 77.8%, respectively, if the LFPS and LVFA were the predominant patterns. Multiple IOPs or a negative MRI did not indicate a poor prognosis. CONCLUSIONS Five distinct IOPs were identified in the patients with TLE. The differences found have important clinical implications and could provide complementary information for surgical decision-making, especially in MRI-negative patients.
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Affiliation(s)
- Deqiu Cui
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Runshi Gao
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Cuiping Xu
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Hao Yan
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Xiaohua Zhang
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Tao Yu
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Guojun Zhang
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China.
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Smith EH, Liou JY, Merricks EM, Davis T, Thomson K, Greger B, House P, Emerson RG, Goodman R, McKhann GM, Sheth S, Schevon C, Rolston JD. Human interictal epileptiform discharges are bidirectional traveling waves echoing ictal discharges. eLife 2022; 11:73541. [PMID: 35050851 PMCID: PMC8813051 DOI: 10.7554/elife.73541] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 01/19/2022] [Indexed: 11/13/2022] Open
Abstract
Interictal epileptiform discharges (IEDs), also known as interictal spikes, are large intermittent electrophysiological events observed between seizures in patients with epilepsy. Although they occur far more often than seizures, IEDs are less studied, and their relationship to seizures remains unclear. To better understand this relationship, we examined multi-day recordings of microelectrode arrays implanted in human epilepsy patients, allowing us to precisely observe the spatiotemporal propagation of IEDs, spontaneous seizures, and how they relate. These recordings showed that the majority of IEDs are traveling waves, traversing the same path as ictal discharges during seizures, and with a fixed direction relative to seizure propagation. Moreover, the majority of IEDs, like ictal discharges, were bidirectional, with one predominant and a second, less frequent antipodal direction. These results reveal a fundamental spatiotemporal similarity between IEDs and ictal discharges. These results also imply that most IEDs arise in brain tissue outside the site of seizure onset and propagate toward it, indicating that the propagation of IEDs provides useful information for localizing the seizure focus.
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Affiliation(s)
- Elliot H Smith
- Department of Neurolosurgery, University of Utah, Salt Lake City, United States
| | - Jyun-You Liou
- Department of Anesthesiology, Weill Cornell Medicine, New York, United States
| | - Edward M Merricks
- Department of Neurology, Columbia University Medical Center, New York CIty, United States
| | - Tyler Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, United States
| | - Kyle Thomson
- Departments of Neurosurgery, University of Utah, Salt Lake City, United States
| | - Bradley Greger
- Department of Bioengineering, Arizona State University, Tempe, United States
| | - Paul House
- Neurosurgical Associates, LLC, Murray, United States
| | | | | | - Guy M McKhann
- Department of Neurological Surgery, Columbia University Medical Center, New York, United States
| | - Sameer Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, United States
| | - Catherine Schevon
- Department of Neurology, Columbia University, New York, United States
| | - John D Rolston
- Departments of Neurosurgery, University of Utah, Salt Lake City, United States
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Klimes P, Peter-Derex L, Hall J, Dubeau F, Frauscher B. Spatio-temporal spike dynamics predict surgical outcome in adult focal epilepsy. Clin Neurophysiol 2021; 134:88-99. [PMID: 34991017 DOI: 10.1016/j.clinph.2021.10.023] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/27/2021] [Accepted: 10/29/2021] [Indexed: 01/05/2023]
Abstract
OBJECTIVE We hypothesized that spatio-temporal dynamics of interictal spikes reflect the extent and stability of epileptic sources and determine surgical outcome. METHODS We studied 30 consecutive patients (14 good outcome). Spikes were detected in prolonged stereo-electroencephalography recordings. We quantified the spatio-temporal dynamics of spikes using the variance of the spike rate, line length and skewness of the spike distribution, and related these features to outcome. We built a logistic regression model, and compared its performance to traditional markers. RESULTS Good outcome patients had more dominant and stable sources than poor outcome patients as expressed by a higher variance of spike rates, a lower variance of line length, and a lower variance of positive skewness (ps < 0.05). The outcome was correctly predicted in 80% of patients. This was better or non-inferior to predictions based on a focal lesion (p = 0.016), focal seizure-onset zone, or complete resection (ps > 0.05). In the five patients where traditional markers failed, spike distribution predicted the outcome correctly. The best results were achieved by 18-h periods or longer. CONCLUSIONS Analysis of spike dynamics shows that surgery outcome depends on strong, single and stable sources. SIGNIFICANCE Our quantitative method has the potential to be a reliable predictor of surgical outcome.
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Affiliation(s)
- Petr Klimes
- Analytical Neurophysiology Lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic.
| | - Laure Peter-Derex
- Analytical Neurophysiology Lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Center for Sleep Medicine and Respiratory Diseases, Lyon University Hospital, Lyon 1 University, Lyon, France; Lyon Neuroscience Research Center, Lyon, France
| | - Jeff Hall
- Montreal Neurological Hospital, McGill University, Montreal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
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Lundstrom BN, Brinkmann BH, Worrell GA. Low frequency novel interictal EEG biomarker for localizing seizures and predicting outcomes. Brain Commun 2021; 3:fcab231. [PMID: 34704030 PMCID: PMC8536865 DOI: 10.1093/braincomms/fcab231] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/17/2021] [Accepted: 08/31/2021] [Indexed: 11/14/2022] Open
Abstract
Localizing hyperexcitable brain tissue to treat focal seizures remains challenging. We want to identify the seizure onset zone from interictal EEG biomarkers. We hypothesize that a combination of interictal EEG biomarkers, including a novel low frequency marker, can predict mesial temporal involvement and can assist in prognosis related to surgical resections. Interictal direct current wide bandwidth invasive EEG recordings from 83 patients implanted with 5111 electrodes were retrospectively studied. Logistic regression was used to classify electrodes and patient outcomes. A feed-forward neural network was implemented to understand putative mechanisms. Interictal infraslow frequency EEG activity was decreased for seizure onset zone electrodes while faster frequencies such as delta (2-4 Hz) and beta-gamma (20-50 Hz) activity were increased. These spectral changes comprised a novel interictal EEG biomarker that was significantly increased for mesial temporal seizure onset zone electrodes compared to non-seizure onset zone electrodes. Interictal EEG biomarkers correctly classified mesial temporal seizure onset zone electrodes with a specificity of 87% and positive predictive value of 80%. These interictal EEG biomarkers also correctly classified patient outcomes after surgical resection with a specificity of 91% and positive predictive value of 87%. Interictal infraslow EEG activity is decreased near the seizure onset zone while higher frequency power is increased, which may suggest distinct underlying physiologic mechanisms. Narrowband interictal EEG power bands provide information about the seizure onset zone and can help predict mesial temporal involvement in seizure onset. Narrowband interictal EEG power bands may be less useful for predictions related to non-mesial temporal electrodes. Together with interictal epileptiform discharges and high-frequency oscillations, these interictal biomarkers may provide prognostic information prior to surgical resection. Computational modelling suggests changes in neural adaptation may be related to the observed low frequency power changes.
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Highly consistent temporal lobe interictal spike networks revealed from foramen ovale electrodes. Clin Neurophysiol 2021; 132:2065-2074. [PMID: 34284241 DOI: 10.1016/j.clinph.2021.06.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 06/18/2021] [Accepted: 06/22/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE A major challenge that limits understanding and treatment of epileptic events from mesial temporal structures comes from our inability to detect and map interictal networks reproducibly using scalp electrodes. Here, we developed a novel approach to map interictal spike networks and demonstrate their relationships to seizure onset and lesions in patients with foramen ovale electrode implantations. METHODS We applied the direct Directed Transfer Function to reveal interictal spike propagation from bilateral foramen ovale electrodes on 10 consecutive patients and co-registered spatially with both seizure onset zones and temporal lobe lesions. RESULTS Highly reproducible, yet unique interictal spike networks were seen for each patient (correlation: 0.93 ± 0.13). Interictal spikes spread in both anterior and posterior directions within each temporal lobe, often reverberating between sites. Spikes propagated to the opposite temporal lobe predominantly through posterior pathways. Patients with structural lesions (N = 4), including tumors and sclerosis, developed reproducible spike networks adjacent to their lesions that were highly lateralized compared to patients without lesions. Only 5% of mesial temporal lobe spikes were time-locked with scalp electrode spikes. Our preliminary observation on two lesional patients suggested that along with lesion location, Interictal spike networks also partially co-registered with seizure onset zones suggesting interrelationship between seizure onset and a subset of spike networks. CONCLUSIONS This is the first demonstration of patient-specific, reproducible interictal spike networks in mesial temporal structures that are closely linked to both temporal lobe lesions and seizure onset zones. SIGNIFICANCE Interictal spike connectivity is a novel approach to map epileptic networks that could help advance invasive and non-invasive epilepsy treatments.
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47
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Parker JJ, Jamiolkowski RM, Grant GA, Le S, Halpern CH. Hybrid Fluoroscopic and Neurophysiological Targeting of Responsive Neurostimulation of the Rolandic Cortex. Oper Neurosurg (Hagerstown) 2021; 21:E180-E186. [PMID: 34133746 DOI: 10.1093/ons/opab182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 04/04/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Precise targeting of cortical surface electrodes to epileptogenic regions defined by anatomic and electrophysiological guideposts remains a surgical challenge during implantation of responsive neurostimulation (RNS) devices. OBJECTIVE To describe a hybrid fluoroscopic and neurophysiological technique for targeting of subdural cortical surface electrodes to anatomic regions with limited direct visualization, such as the interhemispheric fissure. METHODS Intraoperative two-dimensional (2D) fluoroscopy was used to colocalize and align an electrode for permanent device implantation with a temporary in Situ electrode placed for extraoperative seizure mapping. Intraoperative phase reversal mapping technique was performed to distinguish primary somatosensory and motor cortex. RESULTS We applied these techniques to optimize placement of an interhemispheric strip electrode connected to a responsive neurostimulator system for detection and treatment of seizures arising from a large perirolandic cortical malformation. Intraoperative neuromonitoring (IONM) phase reversal technique facilitated neuroanatomic mapping and electrode placement. CONCLUSION In challenging-to-access anatomic regions, fluoroscopy and intraoperative neurophysiology can be employed to augment targeting of neuromodulation electrodes to the site of seizure onset zone or specific neurophysiological biomarkers of clinical interest while minimizing brain retraction.
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Affiliation(s)
- Jonathon J Parker
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
| | - Ryan M Jamiolkowski
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
| | - Gerald A Grant
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
| | - Scheherazade Le
- Department of Neurology, Stanford University School of Medicine, Stanford, California, USA
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
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48
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Cohen N, Ebrahimi Y, Medvedovsky M, Gurevitch G, Aizenstein O, Hendler T, Fahoum F, Gazit T. Interictal Epileptiform Discharge Dynamics in Peri-sylvian Polymicrogyria Using EEG-fMRI. Front Neurol 2021; 12:658239. [PMID: 34149595 PMCID: PMC8212705 DOI: 10.3389/fneur.2021.658239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/05/2021] [Indexed: 11/13/2022] Open
Abstract
Polymicrogyria (PMG) is a common malformation of cortical development associated with a higher susceptibility to epileptic seizures. Seizures secondary to PMG are characterized by difficult-to-localize cerebral sources due to the complex and widespread lesion structure. Tracing the dynamics of interictal epileptiform discharges (IEDs) in patients with epilepsy has been shown to reveal the location of epileptic activity sources, crucial for successful treatment in cases of focal drug-resistant epilepsy. In this case series IED dynamics were evaluated with simultaneous EEG-fMRI recordings in four patients with unilateral peri-sylvian polymicrogyria (PSPMG) by tracking BOLD activations over time: before, during and following IED appearance on scalp EEG. In all cases, focal BOLD activations within the lesion itself preceded the activity associated with the time of IED appearance on EEG, which showed stronger and more widespread activations. We therefore propose that early hemodynamic activity corresponding to IEDs may hold important localizing information potentially leading to the cerebral sources of epileptic activity. IEDs are suggested to develop within a small area in the PSPMG lesion with structural properties obscuring the appearance of their electric field on the scalp and only later engage widespread structures which allow the production of large currents which are recognized as IEDs on EEG.
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Affiliation(s)
- Noa Cohen
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yoram Ebrahimi
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel
| | - Mordekhay Medvedovsky
- Department of Neurology, Agnes Ginges Center of Neurogenetics, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Guy Gurevitch
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Orna Aizenstein
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Department of Diagnostic Imaging, Sourasky Medical Center, Tel Aviv, Israel
| | - Talma Hendler
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.,School of Psychological Science, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Firas Fahoum
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.,Electroencephalography and Epilepsy Unit, Sourasky Medical Center, Tel Aviv, Israel
| | - Tomer Gazit
- Sagol Brain Institute, Wohl Institute for Advanced Imaging, Sourasky Medical Center, Tel Aviv, Israel.,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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49
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Tamilia E, Matarrese MAG, Ntolkeras G, Grant PE, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Noninvasive Mapping of Ripple Onset Predicts Outcome in Epilepsy Surgery. Ann Neurol 2021; 89:911-925. [PMID: 33710676 PMCID: PMC8229023 DOI: 10.1002/ana.26066] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 01/07/2023]
Abstract
OBJECTIVE Intracranial electroencephalographic (icEEG) studies show that interictal ripples propagate across the brain of children with medically refractory epilepsy (MRE), and the onset of this propagation (ripple onset zone [ROZ]) estimates the epileptogenic zone. It is still unknown whether we can map this propagation noninvasively. The goal of this study is to map ripples (ripple zone [RZ]) and their propagation onset (ROZ) using high-density EEG (HD-EEG) and magnetoencephalography (MEG), and to estimate their prognostic value in pediatric epilepsy surgery. METHODS We retrospectively analyzed simultaneous HD-EEG and MEG data from 28 children with MRE who underwent icEEG and epilepsy surgery. Using electric and magnetic source imaging, we estimated virtual sensors (VSs) at brain locations that matched the icEEG implantation. We detected ripples on VSs, defined the virtual RZ and virtual ROZ, and estimated their distance from icEEG. We assessed the predictive value of resecting virtual RZ and virtual ROZ for postsurgical outcome. Interictal spike localization on HD-EEG and MEG was also performed and compared with ripples. RESULTS We mapped ripple propagation in all patients with HD-EEG and in 27 (96%) patients with MEG. The distance from icEEG did not differ between HD-EEG and MEG when mapping the RZ (26-27mm, p = 0.6) or ROZ (22-24mm, p = 0.4). Resecting the virtual ROZ, but not virtual RZ or the sources of spikes, was associated with good outcome for HD-EEG (p = 0.016) and MEG (p = 0.047). INTERPRETATION HD-EEG and MEG can map interictal ripples and their propagation onset (virtual ROZ). Noninvasively mapping the ripple onset may augment epilepsy surgery planning and improve surgical outcome of children with MRE. ANN NEUROL 2021;89:911-925.
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Affiliation(s)
- Eleonora Tamilia
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Margherita A. G. Matarrese
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of EngineeringUniversity Bio‐Medico Campus of RomeRomeItaly
| | - Georgios Ntolkeras
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - P. Ellen Grant
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Joseph R. Madsen
- Epilepsy Surgery Program, Department of NeurosurgeryBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Steve M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMA
| | - Phillip L. Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of NeurologyBoston Children's Hospital, Harvard Medical SchoolBostonMA
| | - Christos Papadelis
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of MedicineBoston Children's Hospital, Harvard Medical SchoolBostonMA
- Jane and John Justin Neurosciences CenterCook Children's Health Care SystemFort WorthTX
- School of Medicine, Texas Christian University and University of North Texas Health Science CenterFort WorthTX
- Department of BioengineeringUniversity of Texas at ArlingtonArlingtonTX
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50
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Li R, Plummer C, Vogrin SJ, Woods WP, Kuhlmann L, Boston R, Liley DTJ, Cook MJ, Grayden DB. Interictal spike localization for epilepsy surgery using magnetoencephalography beamforming. Clin Neurophysiol 2021; 132:928-937. [PMID: 33636608 DOI: 10.1016/j.clinph.2020.12.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 11/25/2020] [Accepted: 12/03/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVE Magnetoencephalography (MEG) kurtosis beamforming is an automated localization method for focal epilepsy. Visual examination of virtual sensors, which are source activities reconstructed by beamforming, can improve performance but can be time-consuming for neurophysiologists. We propose a framework to automate the method and evaluate its effectiveness against surgical resections and outcomes. METHODS We retrospectively analyzed MEG recordings of 13 epilepsy surgery patients who had one-year minimum post-operative follow-up. Kurtosis beamforming was applied and manual inspection was confined to morphological clusters. The region with the Maximum Interictal Spike Frequency (MISF) was validated against prospectively modelled sLORETA solutions and surgical resections linked to outcome. RESULTS Our approach localized spikes in 12 out of 13 patients. In eight patients with Engel I surgical outcomes, beamforming MISF regions were concordant with surgical resection at overlap level for five patients and at lobar level for three patients. The MISF regions localized to spike onset and propagation modelled by sLORETA in two and six patients, respectively. CONCLUSIONS Automated beamforming using MEG can predict postoperative seizure freedom at the lobar level but tends to localize propagated MEG spikes. SIGNIFICANCE MEG beamforming may contribute to non-invasive procedures to predict surgical outcome for patients with drug-refractory focal epilepsy.
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Affiliation(s)
- Rui Li
- Department of Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia.
| | - Chris Plummer
- Department of Medicine, The University of Melbourne, Fitzroy, VIC, Australia; Department of Neurology, St. Vincent's Hospital, Fitzroy, VIC, Australia; School of Health Sciences, Swinburne University of Technology, Hawthorn, VIC, Australia
| | - Simon J Vogrin
- Department of Medicine, The University of Melbourne, Fitzroy, VIC, Australia; Department of Neurology, St. Vincent's Hospital, Fitzroy, VIC, Australia; School of Health Sciences, Swinburne University of Technology, Hawthorn, VIC, Australia
| | - William P Woods
- School of Health Sciences, Swinburne University of Technology, Hawthorn, VIC, Australia
| | - Levin Kuhlmann
- Faculty of Information Technology, Monash University, Clayton, VIC 3168, Australia
| | - Ray Boston
- Department of Medicine, The University of Melbourne, Fitzroy, VIC, Australia; Department of Clinical Studies, New Bolton Centre, School of Veterinary Medicine, University of Pennsylvania, Kennett Square, PA, USA
| | - David T J Liley
- Department of Medicine, The University of Melbourne, Fitzroy, VIC, Australia; Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, VIC, Australia
| | - Mark J Cook
- Department of Medicine, The University of Melbourne, Fitzroy, VIC, Australia; Department of Neurology, St. Vincent's Hospital, Fitzroy, VIC, Australia; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia; Department of Medicine, The University of Melbourne, Fitzroy, VIC, Australia; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia
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