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Wang SH, Arnulfo G, Nobili L, Myrov V, Ferrari P, Ciuciu P, Palva S, Palva JM. Neuronal synchrony and critical bistability: Mechanistic biomarkers for localizing the epileptogenic network. Epilepsia 2024. [PMID: 38687176 DOI: 10.1111/epi.17996] [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: 11/26/2023] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 05/02/2024]
Abstract
OBJECTIVE Postsurgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30% to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). We aimed to advance a novel approach to better characterize epileptogenicity and investigate whether the EZ encompasses a broader epileptogenic network (EpiNet) beyond the seizure zone (SZ) that exhibits seizure activity. METHODS We first used computational modeling to test putative complex systems-driven and systems neuroscience-driven mechanistic biomarkers for epileptogenicity. We then used these biomarkers to extract features from resting-state stereoelectroencephalograms recorded from DRE patients and trained supervised classifiers to localize the SZ against gold standard clinical localization. To further explore the prevalence of pathological features in an extended brain network outside of the clinically identified SZ, we also used unsupervised classification. RESULTS Supervised SZ classification trained on individual features achieved accuracies of .6-.7 area under the receiver operating characteristic curve (AUC). Combining all criticality and synchrony features further improved the AUC to .85. Unsupervised classification discovered an EpiNet-like cluster of brain regions, in which 51% of brain regions were outside of the SZ. Brain regions in the EpiNet-like cluster engaged in interareal hypersynchrony and locally exhibited high-amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure risk regime revealed by our computational modeling. SIGNIFICANCE The finding that combining biomarkers improves SZ localization accuracy indicates that the novel mechanistic biomarkers for epileptogenicity employed here yield synergistic information. On the other hand, the discovery of SZ-like brain dynamics outside of the clinically defined SZ provides empirical evidence of an extended pathophysiological EpiNet.
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Affiliation(s)
- Sheng H Wang
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Le Commissariat à l'énergie atomique et aux énergies alternatives, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
- Models and Inference for Neuroimaging Data, Inria, Palaiseau, France
| | - Gabriele Arnulfo
- Department of Informatics, Bioengineering, Robotics, and System Engineering, University of Genoa, Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, Istituto di Ricovero e Cura a Carattere Scientifico Istituto Giannina Gaslini, Member of the European Reference Network EpiCARE, Genoa, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, and Maternal and Children's Sciences, University of Genoa, Genoa, Italy
| | - Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Paul Ferrari
- Jack H. Miller Magnetoencephalography Center, Helen DeVos Childrens Hospital, Grand Rapids, Michigan, USA
- Department of Pediatrics and Human Development, Michigan State University, East Lansing, Michigan, USA
| | - Philippe Ciuciu
- Le Commissariat à l'énergie atomique et aux énergies alternatives, NeuroSpin, Université Paris-Saclay, Gif-sur-Yvette, France
- Models and Inference for Neuroimaging Data, Inria, Palaiseau, France
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Division of Psychology, Values, Ideologies and Social Contexts of Education, Faculty of Education and Psychology, University of Oulu, Oulu, Finland
| | - J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
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Di Giacomo R, Burini A, Chiarello D, Pelliccia V, Deleo F, Garbelli R, de Curtis M, Tassi L, Gnatkovsky V. Ictal fast activity chirps as markers of the epileptogenic zone. Epilepsia 2024. [PMID: 38686942 DOI: 10.1111/epi.17995] [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: 11/13/2023] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 05/02/2024]
Abstract
The identification of the epileptogenic zone (EZ) boundaries is crucial for effective focal epilepsy surgery. We verify the value of a neurophysiological biomarker of focal ictogenesis, characterized by a low-voltage fast-activity ictal pattern (chirp) recorded with intracerebral electrodes during invasive presurgical monitoring (stereoelectroencephalography [SEEG]). The frequency content of SEEG signals was retrospectively analyzed with semiautomatic software in 176 consecutive patients with focal epilepsies that either were cryptogenic or presented with discordant anatomoelectroclinical findings. Fast activity seizure patterns with the spectrographic features of chirps were confirmed by computer-assisted analysis in 95.4% of patients who presented with heterogeneous etiologies and diverse lobar location of the EZ. Statistical analysis demonstrated (1) correlation between seizure outcome and concordance of sublobar regions included in the EZ defined by visual analysis and chirp-generating regions, (2) high concordance in contact-by contact analysis of 68 patients with Engel class Ia outcome, and (3) that discordance between chirp location and the visually outlined EZ correlated with worse seizure outcome. Seizure outcome analysis confirms the fast activity chirp pattern is a reproducible biomarker of the EZ in a heterogeneous group of patients undergoing SEEG.
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Affiliation(s)
- Roberta Di Giacomo
- Epilepsy Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Alessandra Burini
- Epilepsy Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
- Clinical Neurology, Department of Medicine (DAME), University of Udine, Udine, Italy
| | - Daniela Chiarello
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy
| | - Veronica Pelliccia
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy
| | - Francesco Deleo
- Epilepsy Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Rita Garbelli
- Epilepsy Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Marco de Curtis
- Epilepsy Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Laura Tassi
- "Claudio Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy
| | - Vadym Gnatkovsky
- Epilepsy Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
- Department of Epileptology, Universitätsklinikum Bonn, Bonn, Germany
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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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Bratu IF, Makhalova J, Garnier E, Villalon SM, Jegou A, Bonini F, Lagarde S, Pizzo F, Trébuchon A, Scavarda D, Carron R, Bénar C, Bartolomei F. Permutation entropy-derived parameters to estimate the epileptogenic zone network. Epilepsia 2024; 65:389-401. [PMID: 38041564 DOI: 10.1111/epi.17849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/03/2023]
Abstract
OBJECTIVE Quantification of the epileptogenic zone network (EZN) most frequently implies analysis of seizure onset. However, important information can also be obtained from the postictal period, characterized by prominent changes in the EZN. We used permutation entropy (PE), a measure of signal complexity, to analyze the peri-ictal stereoelectroencephalography (SEEG) signal changes with emphasis on the postictal state. We sought to determine the best PE-derived parameter (PEDP) for identifying the EZN. METHODS Several PEDPs were computed retrospectively on SEEG-recorded seizures of 86 patients operated on for drug-resistant epilepsy: mean baseline preictal entropy, minimum ictal entropy, maximum postictal entropy, the ratio between the maximum postictal and the minimum ictal entropy, and the ratio between the maximum postictal and the baseline preictal entropy. The performance of each biomarker was assessed by comparing the identified epileptogenic contacts or brain regions against the EZN defined by clinical analysis incorporating the Epileptogenicity Index and the connectivity epileptogenicity index methods (EZNc), using the receiver-operating characteristic and precision-recall. RESULTS The ratio between the maximum postictal and the minimum ictal entropy (defined as the Permutation Entropy Index [PEI]) proved to be the best-performing PEDP to identify the EZNC . It demonstrated the highest area under the curve (AUC) and F1 score at the contact level (AUC 0.72; F1 0.39) and at the region level (AUC 0.78; F1 0.47). PEI values gradually decreased between the EZN, the propagation network, and the non-involved regions. PEI showed higher performance in patients with slow seizure-onset patterns than in those with fast seizure-onset patterns. The percentage of resected epileptogenic regions defined by PEI was significantly correlated with surgical outcome. SIGNIFICANCE PEI is a promising tool to improve the delineation of the EZN. PEI combines ease and robustness in a routine clinical setting with high sensitivity for seizures without fast activity at seizure onset.
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Affiliation(s)
- Ionuț-Flavius Bratu
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Julia Makhalova
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
- APHM, Timone Hospital, CEMEREM, Marseille, France
| | - Elodie Garnier
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Samuel Medina Villalon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Aude Jegou
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Francesca Bonini
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Stanislas Lagarde
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Francesca Pizzo
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Agnès Trébuchon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Didier Scavarda
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- APHM Paediatric Neurosurgery Department, Marseille, France
| | - Romain Carron
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- APHM Functional Neurosurgery Department, Marseille, France
| | - Christian Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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Guo F, Cui Y, Li A, Liu M, Jian Z, Chen K, Yao D, Guo D, Xia Y. Differential patterns of very high-frequency oscillations in two seizure types of the pilocarpine-induced TLE model. Brain Res Bull 2023; 204:110805. [PMID: 37925081 DOI: 10.1016/j.brainresbull.2023.110805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 10/08/2023] [Accepted: 10/30/2023] [Indexed: 11/06/2023]
Abstract
AIMS Very high-frequency oscillations (VHFOs, >500 Hz) are considered a highly sensitive biomarker of seizures. We hypothesized that VHFOs may exhibit specificity towards hypersynchronous (HYP) seizures and low-voltage fast (LVF) seizures in temporal lobe epilepsy (TLE). METHODS Local field potentials were recorded from the hippocampal network in TLE mice induced by pilocarpine. Subsequently, we analyzed the VHFO features, including their temporal-frequency characteristics and VHFO/theta coupling, during three states: baseline, preictal, and postictal for both HYP- and LVF-seizure groups. RESULTS Significant changes in most of the VHFO features were observed during the preictal state in both seizure groups. In the postictal state, VHFO features in the HYP-seizure group exhibited inverse alterations and appeared to align with those observed during baseline conditions. However, such phenomena were not observed after TLE seizures in the LVF-seizure group. CONCLUSION Our findings highlight distinct patterns of VHFO feature changes across different states of HYP seizures and LVF seizures. These results suggest that VHFOs could serve as indicative biomarkers for seizure alterations specifically associated with HYP-seizure states.
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Affiliation(s)
- Fengru Guo
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yan Cui
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Airui Li
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Mingqi Liu
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zhaoxin Jian
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Ke Chen
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dezhong Yao
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Daqing Guo
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yang Xia
- Department of Neurosurgery, Sichuan Provincial People's Hospital, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China.
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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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Ghulaxe Y, Joshi A, Chavada J, Huse S, Kalbande B, Sarda PP. Understanding Focal Seizures in Adults: A Comprehensive Review. Cureus 2023; 15:e48173. [PMID: 38046728 PMCID: PMC10693312 DOI: 10.7759/cureus.48173] [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: 08/28/2023] [Accepted: 11/02/2023] [Indexed: 12/05/2023] Open
Abstract
Focal or partial seizures are a common neurological disorder affecting adults. This review aims to provide an in-depth understanding of focal seizures in adults, including their classification, clinical presentation, etiology, diagnosis, and management. This article seeks to enhance awareness and knowledge among medical professionals and the general public by exploring the latest research and clinical insights. Standard electroencephalography (EEG) and recordings in presurgical electrode depth in humans provide a clear definition of patterns similar to focal seizures. Models of animals with partial seizures and epilepsy mimic seizure patterns with comparable characteristics. However, the network factors supporting interictal spikes, as well as the start, development, and end of seizures remain obscure. According to recent research, inhibitory networks are heavily implicated at the beginning of seizures, and extracellular potassium alterations help start and maintain seizure continuation. An increase in network synchronization, which may be caused by both excitatory and inhibitory pathways, is correlated with the cessation of a partial seizure. Recent research on temporal lobe focal seizures in human and animal models leads to the hypothesis that the active blocking of subcortical arousal processes brings on unconsciousness. Brainstem, basal forebrain, and thalamic arousal networks' neuronal firing is diminished during focal limbic seizures, and cortical arousal can be recovered when subcortical arousal circuits are engaged. These results suggest that thalamic neurostimulation may be therapeutic to restore arousal and consciousness during and after seizures. Targeted subcortical stimulation may increase arousal and consciousness when current treatments cannot halt seizures, enhancing safety and psychosocial function for epileptic patients. We embark on an investigation into adult focal seizures in this thorough review that goes beyond a cursory knowledge of their clinical symptoms.
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Affiliation(s)
- Yash Ghulaxe
- Department of Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Abhishek Joshi
- Department of Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Jay Chavada
- Department of Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Shreyash Huse
- Department of Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Bhakti Kalbande
- Department of Dentistry, Sharad Pawar Dental College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Prayas P Sarda
- Department of Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Jirsa V, Wang H, Triebkorn P, Hashemi M, Jha J, Gonzalez-Martinez J, Guye M, Makhalova J, Bartolomei F. Personalised virtual brain models in epilepsy. Lancet Neurol 2023; 22:443-454. [PMID: 36972720 DOI: 10.1016/s1474-4422(23)00008-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 12/20/2022] [Accepted: 01/04/2023] [Indexed: 03/29/2023]
Abstract
Individuals with drug-resistant focal epilepsy are candidates for surgical treatment as a curative option. Before surgery can take place, the patient must have a presurgical evaluation to establish whether and how surgical treatment might stop their seizures without causing neurological deficits. Virtual brains are a new digital modelling technology that map the brain network of a person with epilepsy, using data derived from MRI. This technique produces a computer simulation of seizures and brain imaging signals, such as those that would be recorded with intracranial EEG. When combined with machine learning, virtual brains can be used to estimate the extent and organisation of the epileptogenic zone (ie, the brain regions related to seizure generation and the spatiotemporal dynamics during seizure onset). Virtual brains could, in the future, be used for clinical decision making, to improve precision in localisation of seizure activity, and for surgical planning, but at the moment these models have some limitations, such as low spatial resolution. As evidence accumulates in support of the predictive power of personalised virtual brain models, and as methods are tested in clinical trials, virtual brains might inform clinical practice in the near future.
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Affiliation(s)
- Viktor Jirsa
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France.
| | - Huifang Wang
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Paul Triebkorn
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Meysam Hashemi
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | - Jayant Jha
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France
| | | | - Maxime Guye
- Centre National de la Recherche Scientifique, Center for Magnetic Resonance in Biology and Medicine, Aix Marseille Université, Marseille, France; Centre d'Exploration Métabolique par Résonance Magnétique, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
| | - Julia Makhalova
- Centre National de la Recherche Scientifique, Center for Magnetic Resonance in Biology and Medicine, Aix Marseille Université, Marseille, France; Centre d'Exploration Métabolique par Résonance Magnétique, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France; Epileptology and Clinical Neurophysiology Department, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
| | - Fabrice Bartolomei
- Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Aix Marseille Université, Marseille, France; Epileptology and Clinical Neurophysiology Department, Assistance Publique - Hôpitaux de Marseille, La Timone University Hospital, Marseille, France
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Lai N, Li Z, Xu C, Wang Y, Chen Z. Diverse nature of interictal oscillations: EEG-based biomarkers in epilepsy. Neurobiol Dis 2023; 177:105999. [PMID: 36638892 DOI: 10.1016/j.nbd.2023.105999] [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: 12/02/2022] [Revised: 01/07/2023] [Accepted: 01/09/2023] [Indexed: 01/11/2023] Open
Abstract
Interictal electroencephalogram (EEG) patterns, including high-frequency oscillations (HFOs), interictal spikes (ISs), and slow wave activities (SWAs), are defined as specific oscillations between seizure events. These interictal oscillations reflect specific dynamic changes in network excitability and play various roles in epilepsy. In this review, we briefly describe the electrographic characteristics of HFOs, ISs, and SWAs in the interictal state, and discuss the underlying cellular and network mechanisms. We also summarize representative evidence from experimental and clinical epilepsy to address their critical roles in ictogenesis and epileptogenesis, indicating their potential as electrophysiological biomarkers of epilepsy. Importantly, we put forwards some perspectives for further research in the field.
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Affiliation(s)
- Nanxi Lai
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhisheng Li
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Wang
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhong Chen
- Institute of Pharmacology & Toxicology, NHC and CAMS Key Laboratory of Medical Neurobiology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China; Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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10
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Wang HE, Woodman M, Triebkorn P, Lemarechal JD, Jha J, Dollomaja B, Vattikonda AN, Sip V, Medina Villalon S, Hashemi M, Guye M, Makhalova J, Bartolomei F, Jirsa V. Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy. Sci Transl Med 2023; 15:eabp8982. [PMID: 36696482 DOI: 10.1126/scitranslmed.abp8982] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Precise estimates of epileptogenic zone networks (EZNs) are crucial for planning intervention strategies to treat drug-resistant focal epilepsy. Here, we present the virtual epileptic patient (VEP), a workflow that uses personalized brain models and machine learning methods to estimate EZNs and to aid surgical strategies. The structural scaffold of the patient-specific whole-brain network model is constructed from anatomical T1 and diffusion-weighted magnetic resonance imaging. Each network node is equipped with a mathematical dynamical model to simulate seizure activity. Bayesian inference methods sample and optimize key parameters of the personalized model using functional stereoelectroencephalography recordings of patients' seizures. These key parameters together with their personalized model determine a given patient's EZN. Personalized models were further used to predict the outcome of surgical intervention using virtual surgeries. We evaluated the VEP workflow retrospectively using 53 patients with drug-resistant focal epilepsy. VEPs reproduced the clinically defined EZNs with a precision of 0.6, where the physical distance between epileptogenic regions identified by VEP and the clinically defined EZNs was small. Compared with the resected brain regions of 25 patients who underwent surgery, VEP showed lower false discovery rates in seizure-free patients (mean, 0.028) than in non-seizure-free patients (mean, 0.407). VEP is now being evaluated in an ongoing clinical trial (EPINOV) with an expected 356 prospective patients with epilepsy.
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Affiliation(s)
- Huifang E Wang
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Marmaduke Woodman
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Paul Triebkorn
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Jean-Didier Lemarechal
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Centre MEG-EEG and Experimental Neurosurgery team, Paris F-75013, France
| | - Jayant Jha
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Borana Dollomaja
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Anirudh Nihalani Vattikonda
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Viktor Sip
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Samuel Medina Villalon
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France
| | - Meysam Hashemi
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Maxime Guye
- Aix-Marseille Université, CNRS, CRMBM, Marseille 13005, France.,APHM, Timone University Hospital, CEMEREM, Marseille 13005, France
| | - Julia Makhalova
- APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France.,Aix-Marseille Université, CNRS, CRMBM, Marseille 13005, France.,APHM, Timone University Hospital, CEMEREM, Marseille 13005, France
| | - Fabrice Bartolomei
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.,APHM, Epileptology and Clinical Neurophysiology Department, Timone Hospital, Marseille 13005, France
| | - Viktor Jirsa
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
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11
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Wang ZJ, Noh BH, Kim ES, Yang D, Yang S, Kim NY, Hur YJ, Kim HD. Brain network analysis of interictal epileptiform discharges from ECoG to identify epileptogenic zone in pediatric patients with epilepsy and focal cortical dysplasia type II: A retrospective study. Front Neurol 2022; 13:901633. [PMID: 35989902 PMCID: PMC9388828 DOI: 10.3389/fneur.2022.901633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveFor patients with drug–resistant focal epilepsy, intracranial monitoring remains the gold standard for surgical intervention. Focal cortical dysplasia (FCD) is the most common cause of pharmacoresistant focal epilepsy in pediatric patients who usually develop seizures in early childhood. Timely removal of the epileptogenic zone (EZ) is necessary to achieve lasting seizure freedom and favorable developmental and cognitive outcomes to improve the quality of life. We applied brain network analysis to investigate potential biomarkers for the diagnosis of EZ that will aid in the resection for pediatric focal epilepsy patients with FCD type II.MethodsTen pediatric patients with focal epilepsy diagnosed as FCD type II and that had a follow–up after resection surgery (Engel class I [n = 9] and Engel class II [n = 1]) were retrospectively included. Time–frequency analysis of phase transfer entropy, graph theory analysis, and power spectrum compensation were combined to calculate brain network parameters based on interictal epileptiform discharges from ECoG.ResultsClustering coefficient, local efficiency, node out–degree, and node out–strength with higher values are the most reliable biomarkers for the delineation of EZ, and the differences between EZ and margin zone (MZ), and EZ and normal zone (NZ) were significant (p < 0.05; Mann–Whitney U-test, two–tailed). In particular, the difference between MZ and NZ was significant for patients with frontal FCD (MZ > NZ; p < 0.05) but was not significant for patients with extra–frontal FCD.ConclusionsBrain network analysis, based on the combination of time–frequency analysis of phase transfer entropy, graph theory analysis, and power spectrum compensation, can aid in the diagnosis of EZ for pediatric focal epilepsy patients with FCD type II.
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Affiliation(s)
- Zhi Ji Wang
- Division of Pediatric Neurology, Department of Pediatrics, Severance Children's Hospital, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
- Radio Frequency Integrated Circuit (RFIC), Kwangwoon University, Seoul, South Korea
| | - Byoung Ho Noh
- Department of Pediatrics, Kangwon National University Hospital, Chuncheon-si, South Korea
| | - Eun Seong Kim
- Radio Frequency Integrated Circuit (RFIC), Kwangwoon University, Seoul, South Korea
| | - Donghwa Yang
- Division of Pediatric Neurology, Department of Pediatrics, Severance Children's Hospital, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
- Division of Pediatric Neurology, Department of Pediatrics, National Health Insurance Service Ilsan Hospital, Goyang-si, South Korea
| | - Shan Yang
- Radio Frequency Integrated Circuit (RFIC), Kwangwoon University, Seoul, South Korea
| | - Nam Young Kim
- Radio Frequency Integrated Circuit (RFIC), Kwangwoon University, Seoul, South Korea
| | - Yun Jung Hur
- Department of Pediatrics, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea
- *Correspondence: Yun Jung Hur
| | - Heung Dong Kim
- Division of Pediatric Neurology, Department of Pediatrics, Severance Children's Hospital, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
- Heung Dong Kim
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12
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Li X, Zhang H, Lai H, Wang J, Wang W, Yang X. High-Frequency Oscillations and Epileptogenic Network. Curr Neuropharmacol 2022; 20:1687-1703. [PMID: 34503414 PMCID: PMC9881061 DOI: 10.2174/1570159x19666210908165641] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/26/2021] [Accepted: 08/31/2021] [Indexed: 11/22/2022] Open
Abstract
Epilepsy is a network disease caused by aberrant neocortical large-scale connectivity spanning regions on the scale of several centimeters. High-frequency oscillations, characterized by the 80-600 Hz signals in electroencephalography, have been proven to be a promising biomarker of epilepsy that can be used in assessing the severity and susceptibility of epilepsy as well as the location of the epileptogenic zone. However, the presence of a high-frequency oscillation network remains a topic of debate as high-frequency oscillations have been previously thought to be incapable of propagation, and the relationship between high-frequency oscillations and the epileptogenic network has rarely been discussed. Some recent studies reported that high-frequency oscillations may behave like networks that are closely relevant to the epileptogenic network. Pathological highfrequency oscillations are network-driven phenomena and elucidate epileptogenic network development; high-frequency oscillations show different characteristics coincident with the epileptogenic network dynamics, and cross-frequency coupling between high-frequency oscillations and other signals may mediate the generation and propagation of abnormal discharges across the network.
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Affiliation(s)
- Xiaonan Li
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | | | | | - Jiaoyang Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Wei Wang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
| | - Xiaofeng Yang
- Bioland Laboratory, Guangzhou, China; ,Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China,Address correspondence to this author at the Bioland Laboratory, Guangzhou, China; Tel: 86+ 18515855127; E-mail:
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13
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Makhalova J, Medina Villalon S, Wang H, Giusiano B, Woodman M, Bénar C, Guye M, Jirsa V, Bartolomei F. Virtual Epileptic Patient brain modeling: relationships with seizure onset and surgical outcome. Epilepsia 2022; 63:1942-1955. [PMID: 35604575 PMCID: PMC9543509 DOI: 10.1111/epi.17310] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/29/2022]
Abstract
Objective The virtual epileptic patient (VEP) is a large‐scale brain modeling method based on virtual brain technology, using stereoelectroencephalography (SEEG), anatomical data (magnetic resonance imaging [MRI] and connectivity), and a computational neuronal model to provide computer simulations of a patient's seizures. VEP has potential interest in the presurgical evaluation of drug‐resistant epilepsy by identifying regions most likely to generate seizures. We aimed to assess the performance of the VEP approach in estimating the epileptogenic zone and in predicting surgical outcome. Methods VEP modeling was retrospectively applied in a cohort of 53 patients with pharmacoresistant epilepsy and available SEEG, T1‐weighted MRI, and diffusion‐weighted MRI. Precision recall was used to compare the regions identified as epileptogenic by VEP (EZVEP) to the epileptogenic zone defined by clinical analysis incorporating the Epileptogenicity Index (EI) method (EZC). In 28 operated patients, we compared the VEP results and clinical analysis with surgical outcome. Results VEP showed a precision of 64% and a recall of 44% for EZVEP detection compared to EZC. There was a better concordance of VEP predictions with clinical results, with higher precision (77%) in seizure‐free compared to non‐seizure‐free patients. Although the completeness of resection was significantly correlated with surgical outcome for both EZC and EZVEP, there was a significantly higher number of regions defined as epileptogenic exclusively by VEP that remained nonresected in non‐seizure‐free patients. Significance VEP is the first computational model that estimates the extent and organization of the epileptogenic zone network. It is characterized by good precision in detecting epileptogenic regions as defined by a combination of visual analysis and EI. The potential impact of VEP on improving surgical prognosis remains to be exploited. Analysis of factors limiting the performance of the actual model is crucial for its further development.
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Affiliation(s)
- Julia Makhalova
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Timone Hospital, CEMEREM, Marseille, France
| | - Samuel Medina Villalon
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France.,Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Huifang Wang
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Bernard Giusiano
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.,APHM, Public Health Department, Marseille, France
| | - Marmaduke Woodman
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Christian Bénar
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Maxime Guye
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, Marseille, France.,APHM, Timone Hospital, CEMEREM, Marseille, France
| | - Viktor Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology, Marseille, France.,Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
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14
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Irannejad A, Chaitanya G, Toth E, Pizarro D, Pati S. Direct Cortical Stimulation to Probe the Ictogenicity of the Epileptogenic Nodes in Temporal Lobe Epilepsy. Front Neurol 2022; 12:761412. [PMID: 35095721 PMCID: PMC8793936 DOI: 10.3389/fneur.2021.761412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 12/09/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate mapping of the seizure onset zone (SOZ) is critical to the success of epilepsy surgery outcomes. Epileptogenicity index (EI) is a statistical method that delineates hyperexcitable brain regions involved in the generation and early propagation of seizures. However, EI can overestimate the SOZ for particular electrographic seizure onset patterns. Therefore, using direct cortical stimulation (DCS) as a probing tool to identify seizure generators, we systematically evaluated the causality of the high EI nodes (>0.3) in replicating the patient's habitual seizures. Specifically, we assessed the diagnostic yield of high EI nodes, i.e., the proportion of high EI nodes that evoked habitual seizures. A retrospective single-center study that included post-stereo encephalography (SEEG) confirmed TLE patients (n = 37) that had all high EI nodes stimulated, intending to induce a seizure. We evaluated the nodal responses (true and false responder rate) to stimulation and correlated with electrographic seizure onset patterns (hypersynchronous-HYP and low amplitude fast activity patterns-LAFA) and clinically defined SOZ. The ictogenicity (i.e., the propensity to induce the patient's habitual seizure) of a high EI node was only 44.5%. The LAFA onset pattern had a significantly higher response rate to DCS (i.e., higher evoked seizures). The concordance of an evoked habitual seizure with a clinically defined SOZ with good outcomes was over 50% (p = 0.0025). These results support targeted mapping of SOZ in LAFA onset patterns by performing DCS in high EI nodes to distinguish seizure generators (true responders) from hyperexcitable nodes that may be involved in early propagation.
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Affiliation(s)
- Auriana Irannejad
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Ganne Chaitanya
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Emilia Toth
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Diana Pizarro
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Sandipan Pati
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States
- Epilepsy and Cognitive Neurophysiology Laboratory, University of Alabama at Birmingham, Birmingham, AL, United States
- *Correspondence: Sandipan Pati
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15
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Numata-Uematsu Y, Uematsu M, Sakuraba R, Iwasaki M, Osawa S, Jin K, Nakasato N, Kure S. The Onset of Interictal Spike-Related Ripples Facilitates Detection of the Epileptogenic Zone. Front Neurol 2021; 12:724417. [PMID: 34803874 PMCID: PMC8599368 DOI: 10.3389/fneur.2021.724417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 10/14/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Accurate estimation of the epileptogenic zone (EZ) is essential for favorable outcomes in epilepsy surgery. Conventional ictal electrocorticography (ECoG) onset is generally used to detect the EZ but is insufficient in achieving seizure-free outcomes. By contrast, high-frequency oscillations (HFOs) could be useful markers of the EZ. Hence, we aimed to detect the EZ using interictal spikes and investigated whether the onset area of interictal spike-related HFOs was within the EZ. Methods: The EZ is considered to be included in the resection area among patients with seizure-free outcomes after surgery. Using a complex demodulation technique, we developed a method to determine the onset channels of interictal spike-related ripples (HFOs of 80-200 Hz) and investigated whether they are within the resection area. Results: We retrospectively examined 12 serial patients who achieved seizure-free status after focal resection surgery. Using the method that we developed, we determined the onset channels of interictal spike-related ripples and found that for all 12 patients, they were among the resection channels. The onset frequencies of ripples were in the range of 80-150 Hz. However, the ictal onset channels (evaluated based on ictal ECoG patterns) and ripple onset channels coincided in only 3 of 12 patients. Conclusions: Determining the onset area of interictal spike-related ripples could facilitate EZ estimation. This simple method that utilizes interictal ECoG may aid in preoperative evaluation and improve epilepsy surgery outcomes.
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Affiliation(s)
| | - Mitsugu Uematsu
- Department of Pediatrics, Tohoku University School of Medicine, Sendai, Japan
| | - Rie Sakuraba
- Department of Epileptology, Tohoku University School of Medicine, Sendai, Japan
| | - Masaki Iwasaki
- Department of Neurosurgery, Tohoku University School of Medicine, Sendai, Japan.,Department of Neurosurgery, National Center Hospital of Neurology and Psychiatry, Tokyo, Japan
| | - Shinichiro Osawa
- Department of Neurosurgery, Tohoku University School of Medicine, Sendai, Japan
| | - Kazutaka Jin
- Department of Epileptology, Tohoku University School of Medicine, Sendai, Japan
| | - Nobukazu Nakasato
- Department of Epileptology, Tohoku University School of Medicine, Sendai, Japan
| | - Shigeo Kure
- Department of Pediatrics, Tohoku University School of Medicine, Sendai, Japan
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16
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Impaired awareness in mesial temporal lobe epilepsy: Network analysis of foramen ovale and scalp EEG. Clin Neurophysiol 2021; 132:3084-3094. [PMID: 34717226 DOI: 10.1016/j.clinph.2021.09.011] [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: 05/04/2021] [Revised: 08/11/2021] [Accepted: 09/01/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE We use co-registration of foramen-ovale and scalp-EEG to investigate network alterations in temporal-lobe epilepsy during focal seizures without (aura) or with impairment of awareness (SIA). METHODS One aura and one SIA were selected from six patients. Temporal dynamic among 4 epochs, as well as the differences between aura and SIA, were analyzed through partial directed coherence and graph theory-based indices of centrality. RESULTS Regarding the auras temporal evolution, fronto-parietal (FP) regions showed decreased connectivity with respect to the interictal period, in both epileptogenic (EH) and non-epileptogenic hemisphere (nEH). During SIAs, temporal dynamic showed more changes than auras: centrality of mesial temporal (mT) regions changes during all conditions, and nEH FP centrality showed the same dynamic trend of the aura (decreased centrality), until the last epoch, close to the impaired awareness, when showed increased centrality. Comparing SIA with aura, in proximity of impaired awareness, increased centrality was found in all the regions, except in nEH mT. CONCLUSIONS Our findings suggested that the impairment of awareness is related to network alterations occurring first in neocortical regions and when awareness is still retained. SIGNIFICANCE The analysis of 'hub' alteration can represent a suitable biomarker for scalp EEG-based prediction of awareness impairment.
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17
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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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18
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Swarup O, Waxmann A, Chu J, Vogrin S, Lai A, Laing J, Barker J, Seiderer L, Ignatiadis S, Plummer C, Carne R, Seneviratne U, Cook M, Murphy M, D'Souza W. Long-term mood, quality of life, and seizure freedom in intracranial EEG epilepsy surgery. Epilepsy Behav 2021; 123:108241. [PMID: 34450387 DOI: 10.1016/j.yebeh.2021.108241] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 07/21/2021] [Accepted: 07/24/2021] [Indexed: 12/01/2022]
Abstract
OBJECTIVES To determine the long-term outcomes in patients undergoing intracranial EEG (iEEG) evaluation for epilepsy surgery in terms of seizure freedom, mood, and quality of life at St. Vincent's Hospital, Melbourne. METHODS Patients who underwent iEEG between 1999 and 2016 were identified. Patients were retrospectively assessed between 2014 and 2017 by specialist clinic record review and telephone survey with standardized validated questionnaires for: 1) seizure freedom using the Engel classification; 2) Mood using the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E); 3) Quality-of-life outcomes using the QOLIE-10 questionnaire. Summary statistics and univariate analysis were performed to investigate variables for significance. RESULTS Seventy one patients underwent iEEG surgery: 49 Subdural, 14 Depths, 8 Combination with 62/68 (91.9%) of those still alive, available at last follow-up by telephone survey or medical record review (median of 8.2 years). The estimated epileptogenic zone was 62% temporal and 38% extra-temporal. At last follow-up, 69.4% (43/62) were Engel Class I and 30.6% (19/62) were Engel Class II-IV. Further, a depressive episode (NDDI-E > 15)was observed in 34% (16/47), while a 'better quality of life' (QOLIE-10 score < 25) was noted in 74% (31/42). Quality of life (p < 0.001) but not mood (p = 0.24) was associated with seizure freedom. SIGNIFICANCE Long-term seizure freedom can be observed in patients undergoing complex epilepsy surgery with iEEG evaluation and is associated with good quality of life.
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Affiliation(s)
- Oshi Swarup
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia; The University of Melbourne, Grattan Street, Parkville, Victoria 3010, Australia; Department of Medicine, Royal Melbourne Hospital, 300 Grattan Street, Parkville, Melbourne, Victoria 3050, Australia.
| | - Alexandra Waxmann
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia; The University of Melbourne, Grattan Street, Parkville, Victoria 3010, Australia
| | - Jocelyn Chu
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia
| | - Simon Vogrin
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia; The University of Melbourne, Grattan Street, Parkville, Victoria 3010, Australia; Faculty of Health Arts and Design, Swinburne University of Technology, John St, Hawthorn, Victoria 3122, Australia
| | - Alan Lai
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia; The University of Melbourne, Grattan Street, Parkville, Victoria 3010, Australia
| | - Joshua Laing
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia
| | - James Barker
- The University of Melbourne, Grattan Street, Parkville, Victoria 3010, Australia; Department of Medicine, Royal Melbourne Hospital, 300 Grattan Street, Parkville, Melbourne, Victoria 3050, Australia
| | - Linda Seiderer
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia
| | - Sophia Ignatiadis
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia
| | - Chris Plummer
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia; The University of Melbourne, Grattan Street, Parkville, Victoria 3010, Australia; Faculty of Health Arts and Design, Swinburne University of Technology, John St, Hawthorn, Victoria 3122, Australia
| | - Ross Carne
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia
| | - Udaya Seneviratne
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia; The University of Melbourne, Grattan Street, Parkville, Victoria 3010, Australia; Department of Neurosciences, Monash Medical Centre, 246 Clayton Rd, Clayton, Victoria 3168, Australia
| | - Mark Cook
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia; The University of Melbourne, Grattan Street, Parkville, Victoria 3010, Australia
| | - Michael Murphy
- Department of Surgery, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia
| | - Wendyl D'Souza
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, 41 Victoria Parade, Fitzroy, Victoria 3065, Australia; The University of Melbourne, Grattan Street, Parkville, Victoria 3010, Australia
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Smith G, Stacey WC. The accuracy of quantitative EEG biomarker algorithms depends upon seizure onset dynamics. Epilepsy Res 2021; 176:106702. [PMID: 34229226 DOI: 10.1016/j.eplepsyres.2021.106702] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/05/2021] [Accepted: 06/22/2021] [Indexed: 01/01/2023]
Abstract
OBJECTIVE To compare the performance of different ictal quantitative biomarkers of the seizure onset zone (SOZ) across many seizures in a cohort of consecutive patients with a variety of seizure onset patterns. METHODS The Epileptogenicity Index (EI, a measure of fast activity) and Slow Polarizing Shift index (SPS, a measure of infraslow activity) were calculated for 212 seizures (22 patients). After stratification by onset pattern, median index values inside and outside the SOZ were compared in aggregate and for each of the onset patterns. Receiver Operating Characteristic (ROC) curves were constructed to compare the performance of each index. RESULTS Median values of EI (0.056 vs 0.0087), SPS (0.27 vs 0.19), and CI (0.21 vs 0.12) were significantly higher for contacts inside the SOZ, all p < 0.0001. Analysis of AUC showed variable performance of these indices across seizure types, although AUC for EI and SPS was generally greatest for seizures with fast activity at onset. CONCLUSIONS All indices were significantly higher for contacts inside the SOZ; however, the performance of these indices varied depending on the pattern of seizure onset. SIGNIFICANCE These findings suggest that future studies of quantitative biomarkers of the SOZ should account for seizure onset pattern.
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Affiliation(s)
- Garnett Smith
- Department of Pediatrics, Division of Pediatric Neurology, University of Michigan, 1540 E Hospital Drive, Box 4279, Ann Arbor, MI, 48109-4279, USA.
| | - William C Stacey
- Department of Neurology, University of Michigan, 1500 E Medical Center Drive, SPC 5316, Ann Arbor, MI, 48109-5316, USA; Department of Biomedical Engineering, University of Michigan, 1500 E Medical Center Drive, SPC 5316, Ann Arbor, MI, 48109-5316, USA; Biointerfaces Institute, University of Michigan, 1500 E Medical Center Drive, SPC 5316, Ann Arbor, MI, 48109-5316, USA.
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20
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Bosl WJ, Leviton A, Loddenkemper T. Prediction of Seizure Recurrence. A Note of Caution. Front Neurol 2021; 12:675728. [PMID: 34054713 PMCID: PMC8155381 DOI: 10.3389/fneur.2021.675728] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/20/2021] [Indexed: 12/31/2022] Open
Abstract
Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.
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Affiliation(s)
- William J Bosl
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Health Informatics Program, University of San Francisco, San Francisco, CA, United States
| | - Alan Leviton
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Tobias Loddenkemper
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
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21
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Abstract
Epilepsy is characterized by specific alterations in network organization. The main parameters at the basis of epileptogenic network formation are alterations of cortical thickness, development of pathologic hubs, modification of hub distribution, and white matter alterations. The effect is a reinforcement of brain connectivity in both the epileptogenic zone and the propagation zone. Moreover, the epileptogenic network is characterized by some specific neurophysiologic biomarkers that evidence the tendency of the network itself to shift from an interictal state to an ictal one. The recognition of these features is crucial in planning epilepsy surgery.
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22
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Graph energy based centrality measures to detect epileptogenic focal invasive EEG electrodes. Seizure 2021; 85:127-137. [PMID: 33461031 DOI: 10.1016/j.seizure.2020.12.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 12/28/2020] [Accepted: 12/29/2020] [Indexed: 11/22/2022] Open
Abstract
PURPOSE Medically intractable epilepsy can be treated with surgical interventions, which require localization of the cortical region where seizures start. This region is referred to as the epileptogenic zone (EZ). Good surgical outcomes depend on an exact localization of the EZ. METHODS We propose a graph theoretical approach providing a novel method to localize the epileptogenic zone using invasive electroencephalogram (EEG) data. The proposed methods employ centrality determination using three graph energies, namely simple graph energy, Laplacian energy, and distance energy. Centrality values of invasive EEG electrodes from 19 patients were analyzed at different frequency bands and at different time points. K-means clustering was used to distinguish focal (electrodes placed in the epileptogenic zone) from non-focal electrodes using the centrality values obtained. RESULTS Focal electrodes show higher centrality values when compared to non-focal electrodes. All three graph energy based centrality measures proposed show maximum f-score and accuracy during the early seizure phase in the gamma frequency band. Among the three proposed methods, simple graph energy based centrality outperforms Laplacian centrality and distance energy based centrality and also other related and competitive methods available in the literature in terms of accuracy and f-score. CONCLUSION Graph energy based centrality measures are useful parameters for the delineation of the epileptogenic zone. Among the three centrality measures examined, simple graph energy based centrality proved best suited for this purpose.
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23
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Xiao L, Li C, Wang Y, Chen J, Si W, Yao C, Li X, Duan C, Heng PA. Automatic Localization of Seizure Onset Zone From High-Frequency SEEG Signals: A Preliminary Study. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2021. [DOI: 10.1109/jtehm.2021.3090214] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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24
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Association of cortical spreading depression and seizures in patients with medically intractable epilepsy. Clin Neurophysiol 2020; 131:2861-2874. [PMID: 33152524 DOI: 10.1016/j.clinph.2020.09.016] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 08/14/2020] [Accepted: 09/07/2020] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Monitoring of the ultra-low frequency potentials, particularly cortical spreading depression (CSD), is excluded in epilepsy monitoring due to technical barriers imposed by the scalp ultra-low frequency electroencephalogram (EEG). As a result, clinical studies of CSD have been limited to invasive EEG. Therefore, the occurrence of CSD and its interaction with epileptiform field potentials (EFP) require investigation in epilepsy monitoring. METHODS Using a novel AC/DC-EEG approach, the occurrence of DC potentials in patients with intractable epilepsy presenting different symptoms of aura was investigated during long-term video-EEG monitoring. RESULTS Various forms of slow potentials, including simultaneous negative direct current (DC) potentials and prolonged EFP, propagated negative DC potentials, and non-propagated single negative DC potentials were recorded from the scalp of the epileptic patients. The propagated and single negative DC potentials preceded the prolonged EFP with a time lag and seizure appeared at the final shoulder of some instances of the propagated negative DC potentials. The slow potential deflections had a high amplitude and prolonged duration and propagated slowly through the brain. The high-frequency EEG was suppressed in the vicinity of the negative DC potential propagations. CONCLUSIONS The study is the first to report the recording of the propagated and single negative DC potentials with EFP at the scalp of patients with intractable epilepsy. The negative DC potentials preceded the prolonged EFP and may trigger seizures. The propagated and single negative DC potentials may be considered as CSD. SIGNIFICANCE Recordings of CSD may serve as diagnostic and prognostic monitoring tools in epilepsy.
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25
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An N, Ye X, Liu Q, Xu J, Zhang P. Localization of the epileptogenic zone based on ictal stereo-electroencephalogram: Brain network and single-channel signal feature analysis. Epilepsy Res 2020; 167:106475. [PMID: 33045665 DOI: 10.1016/j.eplepsyres.2020.106475] [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: 11/13/2019] [Revised: 06/22/2020] [Accepted: 09/17/2020] [Indexed: 01/21/2023]
Abstract
Accurate localization of the epileptogenic zone (EZ) is crucial for refractory focal epilepsy patients to achieve freedom from seizures following epilepsy surgery. In this study, ictal stereo-electroencephalography data from 35 patients with refractory focal epilepsy were analyzed. Effective networks based on partial directed coherence were analyzed, and a gray level co-occurrence matrix was applied to extract the time-varying features of the in-degree. These features, combined with the single-channel signal time-frequency features, including approximate entropy and line length, were used to localize the EZ based on a cluster algorithm. For all seizure-free patients (n = 23), the proposed method was effective in identifying the clinical-EZ-contacts and clinical-EZ-blocks, with an F1-score of 62.47 % and 72.18 %, respectively. The sensitivity was 96.00 % for the clinical-EZ-block identification, which provided the information for the decision-making of clinicians, prompting clinicians to focus on the identified EZ-blocks and their nearby contacts. The agreement between the EZ identified by the proposed method and the clinical-EZ was worse for non-seizure-free patients (n = 12) than for seizure-free patients. Furthermore, our method provided better results than using only brain network or single-channel signal features. This suggests that combining these complementary features can facilitate more accurate localization of the EZ.
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Affiliation(s)
- Nan An
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Xiaolai Ye
- Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Qiangqiang Liu
- Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Jiwen Xu
- Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Puming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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26
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Demuru M, Kalitzin S, Zweiphenning W, van Blooijs D, Van't Klooster M, Van Eijsden P, Leijten F, Zijlmans M. The value of intra-operative electrographic biomarkers for tailoring during epilepsy surgery: from group-level to patient-level analysis. Sci Rep 2020; 10:14654. [PMID: 32887896 PMCID: PMC7474097 DOI: 10.1038/s41598-020-71359-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 04/23/2020] [Indexed: 01/08/2023] Open
Abstract
Signal analysis biomarkers, in an intra-operative setting, may be complementary tools to guide and tailor the resection in drug-resistant focal epilepsy patients. Effective assessment of biomarker performances are needed to evaluate their clinical usefulness and translation. We defined a realistic ground-truth scenario and compared the effectiveness of different biomarkers alone and combined to localize epileptogenic tissue during surgery. We investigated the performances of univariate, bivariate and multivariate signal biomarkers applied to 1 min inter-ictal intra-operative electrocorticography to discriminate between epileptogenic and non-epileptogenic locations in 47 drug-resistant people with epilepsy (temporal and extra-temporal) who had been seizure-free one year after the operation. The best result using a single biomarker was obtained using the phase-amplitude coupling measure for which the epileptogenic tissue was localized in 17 out of 47 patients. Combining the whole set of biomarkers provided an improvement of the performances: 27 out of 47 patients. Repeating the analysis only on the temporal-lobe resections we detected the epileptogenic tissue in 29 out of 30 combining all the biomarkers. We suggest that the assessment of biomarker performances on a ground-truth scenario is required to have a proper estimate on how biomarkers translate into clinical use. Phase-amplitude coupling seems the best performing single biomarker and combining biomarkers improves localization of epileptogenic tissue. Performance achieved is not adequate as a tool in the operation theater yet, but it can improve the understanding of pathophysiological process.
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Affiliation(s)
- Matteo Demuru
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands.
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Stiliyan Kalitzin
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Willemiek Zweiphenning
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Dorien van Blooijs
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maryse Van't Klooster
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pieter Van Eijsden
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Frans Leijten
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maeike Zijlmans
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
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27
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Balatskaya A, Roehri N, Lagarde S, Pizzo F, Medina S, Wendling F, Bénar CG, Bartolomei F. The “Connectivity Epileptogenicity Index ” (cEI), a method for mapping the different seizure onset patterns in StereoElectroEncephalography recorded seizures. Clin Neurophysiol 2020; 131:1947-1955. [DOI: 10.1016/j.clinph.2020.05.029] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 04/15/2020] [Accepted: 05/15/2020] [Indexed: 10/24/2022]
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28
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Uribe San Martin R, Di Giacomo R, Mai R, Gozzo F, Pelliccia V, Mariani V, Cardinale F, Ciampi E, Onofrj M, Tassi L. Forecasting Seizure Freedom After Epilepsy Surgery Assessing Concordance Between Noninvasive and StereoEEG Findings. Neurosurgery 2020; 88:113-121. [DOI: 10.1093/neuros/nyaa322] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 05/24/2020] [Indexed: 11/14/2022] Open
Abstract
Abstract
BACKGROUND
Accurate localization of the probable Epileptogenic Zone (EZ) from presurgical studies is crucial for achieving good prognosis in epilepsy surgery.
OBJECTIVE
To evaluate the degree of concordance at a sublobar localization derived from noninvasive studies (video electroencephalography, EEG; magnetic resonance imaging, MRI; 18-fluorodeoxyglucose positron emission tomography FDG-PET, FDG-PET) and EZ estimated by stereoEEG, in forecasting seizure recurrence in a long-term cohort of patients with focal drug-resistant epilepsy.
METHODS
We selected patients with a full presurgical evaluation and with postsurgical outcome at least 1 yr after surgery. Multivariate Cox regression analysis for seizure freedom (Engel Ia) was performed.
RESULTS
A total of 74 patients were included, 62.2% were in Engel class Ia with a mean follow-up of 2.8 + 2.4 yr after surgery. In the multivariate analysis for Engel Ia vs >Ib, complete resection of the EZ found in stereoEEG (hazard ratio, HR: 0.24, 95%CI: 0.09-0.63, P = .004) and full concordance between FDG-PET and stereoEEG (HR: 0.11, 95%CI: 0.02-0.65, P = .015) portended a more favorable outcome. Most of our results were maintained when analyzing subgroups of patients.
CONCLUSION
The degree of concordance between noninvasive studies and stereoEEG may help to forecast the likelihood of cure before performing resective surgery, particularly using a sublobar classification and comparing the affected areas in the FDG-PET with EZ identified with stereoEEG.
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Affiliation(s)
- Reinaldo Uribe San Martin
- Neurology Department, Pontificia Universidad Católica de Chile, Neurology Service, Complejo Asistencial Hospital Sótero del Río, Santiago, Chile
| | - Roberta Di Giacomo
- Clinical Epileptology and Experimental Neurophysiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy
| | - Roberto Mai
- “Claudio Munari” Epilepsy Surgery Centre, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - Francesca Gozzo
- “Claudio Munari” Epilepsy Surgery Centre, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - Veronica Pelliccia
- “Claudio Munari” Epilepsy Surgery Centre, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - Valeria Mariani
- “Claudio Munari” Epilepsy Surgery Centre, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - Francesco Cardinale
- “Claudio Munari” Epilepsy Surgery Centre, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - Ethel Ciampi
- Neurology Department, Pontificia Universidad Católica de Chile, Neurology Service, Complejo Asistencial Hospital Sótero del Río, Santiago, Chile
| | - Marco Onofrj
- Department of Neuroscience, Imaging and Clinical Sciences, University G. D’Annunzio of Chieti-Pescara, Italy
| | - Laura Tassi
- “Claudio Munari” Epilepsy Surgery Centre, Niguarda Hospital, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
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Jobst BC, Bartolomei F, Diehl B, Frauscher B, Kahane P, Minotti L, Sharan A, Tardy N, Worrell G, Gotman J. Intracranial EEG in the 21st Century. Epilepsy Curr 2020; 20:180-188. [PMID: 32677484 PMCID: PMC7427159 DOI: 10.1177/1535759720934852] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Intracranial electroencephalography (iEEG) has been the mainstay of identifying the seizure onset zone (SOZ), a key diagnostic procedure in addition to neuroimaging when considering epilepsy surgery. In many patients, iEEG has been the basis for resective epilepsy surgery, to date still the most successful treatment for drug-resistant epilepsy. Intracranial EEG determines the location and resectability of the SOZ. Advances in recording and implantation of iEEG provide multiple options in the 21st century. This not only includes the choice between subdural electrodes (SDE) and stereoelectroencephalography (SEEG) but also includes the implantation and recordings from microelectrodes. Before iEEG implantation, especially in magnetic resonance imaging -negative epilepsy, a clear hypothesis for seizure generation and propagation should be based on noninvasive methods. Intracranial EEG implantation should be planned by a multidisciplinary team considering epileptic networks. Recordings from SDE and SEEG have both their advantages and disadvantages. Stereo-EEG seems to have a lower rate of complications that are clinically significant, but has limitations in spatial sampling of the cortical surface. Stereo-EEG can sample deeper areas of the brain including deep sulci and hard to reach areas such as the insula. To determine the epileptogenic zone, interictal and ictal information should be taken into consideration. Interictal spiking, low frequency slowing, as well as high frequency oscillations may inform about the epileptogenic zone. Ictally, high frequency onsets in the beta/gamma range are usually associated with the SOZ, but specialized recordings with combined macro and microelectrodes may in the future educate us about onset in higher frequency bands. Stimulation of intracranial electrodes triggering habitual seizures can assist in identifying the SOZ. Advanced computational methods such as determining the epileptogenicity index and similar measures may enhance standard clinical interpretation. Improved techniques to record and interpret iEEG may in the future lead to a greater proportion of patients being seizure free after epilepsy surgery.
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Affiliation(s)
- Barbara C Jobst
- Geisel School of Medicine at Dartmouth and Dartmouth-Hitchcock Medical Center, Hanover, NH, USA
| | - Fabrice Bartolomei
- Aix Marseille University, INSERM, INS, Inst Neurosci Syst, Marseille, France.,APHM, Timone hospital, Epileptology department, Marseille, France
| | - Beate Diehl
- National Hospital for Neurology and Neurosurgery, University College London, London, United Kingdom
| | - Birgit Frauscher
- Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada
| | - Philippe Kahane
- Neurology Department & INSERM U1216, Grenoble-Alpes University and Hospital, Grenoble, France
| | - Lorella Minotti
- Neurology Department & INSERM U1216, Grenoble-Alpes University and Hospital, Grenoble, France
| | - Ashwini Sharan
- National Hospital for Neurology and Neurosurgery, Jefferson University, Philadelphia, PA, USA
| | - Nastasia Tardy
- Neurology Department & INSERM U1216, Grenoble-Alpes University and Hospital, Grenoble, France
| | | | - Jean Gotman
- Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada
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30
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Abstract
During the presurgical evaluation of patients with focal refractory epilepsies, the spatial mapping of the seizure onset zone (SOZ) and seizure propagation networks critically depends on the use of different features extracted from the intracranial electroencephalogram (IEEG). The identification of the SOZ is usually based on visual inspection by highly qualified neurophysiologists. However, quantitative IEEG analyses have recently been developed by exploiting signal and image characteristics in order to improve and expedite the SOZ detection. Here, the authors briefly review some of the latest methods proposed by different research groups and then present the recent implementation in Brainstorm software.
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31
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Ashourvan A, Pequito S, Khambhati AN, Mikhail F, Baldassano SN, Davis KA, Lucas TH, Vettel JM, Litt B, Pappas GJ, Bassett DS. Model-based design for seizure control by stimulation. J Neural Eng 2020; 17:026009. [PMID: 32103826 PMCID: PMC8341467 DOI: 10.1088/1741-2552/ab7a4e] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Current brain stimulation paradigms are largely empirical rather than theoretical. An opportunity exists to improve upon their modest effectiveness in closed-loop control strategies with the development of theoretically grounded, model-based designs. APPROACH Inspired by this need, here we couple experimental data and mathematical modeling with a control-theoretic strategy for seizure termination. We begin by exercising a dynamical systems approach to model seizures (n = 94) recorded using intracranial EEG (iEEG) from 21 patients with medication-resistant, localization-related epilepsy. MAIN RESULTS Although each patient's seizures displayed unique spatial and temporal patterns, their evolution can be parsimoniously characterized by the same model form. Idiosyncracies of the model can inform individualized intervention strategies, specifically in iEEG samples with well-localized seizure onset zones. Temporal fluctuations in the spatial profiles of the oscillatory modes show that seizure onset marks a transition into a regime in which the underlying system supports prolonged rhythmic and focal activity. Based on these observations, we propose a control-theoretic strategy that aims to stabilize ictal activity using static output feedback for linear time-invariant switching systems. Finally, we demonstrate in silico that our proposed strategy allows us to dampen the emerging focal oscillatory sources using only a small set of electrodes. SIGNIFICANCE Our integrative study informs the development of modulation and control algorithms for neurostimulation that could improve the effectiveness of implantable, closed-loop anti-epileptic devices.
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Affiliation(s)
- Arian Ashourvan
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, United States of America. U.S. Army Research Laboratory, Aberdeen Proving Ground, MD 21005, United States of America
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Vila-Vidal M, Pérez Enríquez C, Principe A, Rocamora R, Deco G, Tauste Campo A. Low entropy map of brain oscillatory activity identifies spatially localized events: A new method for automated epilepsy focus prediction. Neuroimage 2019; 208:116410. [PMID: 31785422 DOI: 10.1016/j.neuroimage.2019.116410] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 11/07/2019] [Accepted: 11/26/2019] [Indexed: 10/25/2022] Open
Abstract
The spatial mapping of localized events in brain activity critically depends on the correct identification of the pattern signatures associated with those events. For instance, in the context of epilepsy research, a number of different electrophysiological patterns have been associated with epileptogenic activity. Motivated by the need to define automated seizure focus detectors, we propose a novel data-driven algorithm for the spatial identification of localized events that is based on the following rationale: the distribution of emerging oscillations during confined events across all recording sites is highly non-uniform and can be mapped using a spatial entropy function. By applying this principle to EEG recording obtained from 67 distinct seizure epochs, our method successfully identified the seizure focus on a group of ten drug-resistant temporal lobe epilepsy patients (average sensitivity: 0.94, average specificity: 0.90) together with its characteristic electrophysiological pattern signature. Cross-validation of the method outputs with postresective information revealed the consistency of our findings in long follow-up seizure-free patients. Overall, our methodology provides a reliable computational procedure that might be used as in both experimental and clinical domains to identify the neural populations undergoing an emerging functional or pathological transition.
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Affiliation(s)
- Manel Vila-Vidal
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain.
| | | | - Alessandro Principe
- Hospital del Mar Medical Research Institute, 08003, Barcelona, Spain; Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar, 08003, Barcelona, Spain; Faculty of Health and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Rodrigo Rocamora
- Hospital del Mar Medical Research Institute, 08003, Barcelona, Spain; Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar, 08003, Barcelona, Spain; Faculty of Health and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Spain.
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats, 08010, Barcelona, Spain
| | - Adrià Tauste Campo
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain.
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Job AS, David O, Minotti L, Bartolomei F, Chabardès S, Kahane P. Epileptogenicity Maps of Intracerebral Fast Activities (60-100 Hz) at Seizure Onset in Epilepsy Surgery Candidates. Front Neurol 2019; 10:1263. [PMID: 31849823 PMCID: PMC6892969 DOI: 10.3389/fneur.2019.01263] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 11/13/2019] [Indexed: 11/13/2022] Open
Abstract
Fast activities (FA) at seizure onset have been increasingly described as a useful signature of the epileptogenic zone (EZ) in patients undergoing intracranial EEG recordings. Different computer-based signal analysis methods have thus been developed for objectively quantifying ictal FA. Whether these methods detect FA in all forms of focal epilepsies, whether they provide similar information than visual analysis (VA), and whether they might help for the surgical decision remain crucial issues. We thus conducted a retrospective study in 21 consecutive patients suffering from drug-resistant seizures studied by SEEG recordings. Ictal FA were quantified using the Epileptogenicity Maps (EM) method that we recently developed and which generates, by adopting a neuroimaging approach, statistical parametric maps of FA ranging from 60 to 100 Hz (FA60−100). Ictal FA were analyzed blindly using VA and EM, and the prognostic significance of removing areas exhibiting FA60−100 at seizure onset was evaluated. A significant ictal FA60−100 activation was found in all patients, and in 92.6% of all the 68 seizures recorded, whatever the epilepsy type. The overlap ratio (OR) between VA and EM was significantly better for defining the regions spared at seizure onset than those from which seizure arose (p < 0.001), especially in temporal or temporal “plus” epilepsies. EM and VA were much more discordant to define the EZ, with a mean number of electrode contacts involved at seizure onset significantly higher with EM than with VA (p = <0.0001). Seizure outcome correlated with the resection ratio for FA60−100, which was significantly higher in seizure-free (Engel's class Ia) than in non seizure-free patients (class Ic-IV) (p = 0.048). The quantification of FA at seizure onset can bring information additional to clinical expertise that might contribute to define accurately the cortical region to be resected.
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Affiliation(s)
- Anne-Sophie Job
- Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Olivier David
- Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Université Grenoble Alpes, Grenoble, France.,INS, Inserm, U1106, Marseille, France
| | - Lorella Minotti
- Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Fabrice Bartolomei
- INS, Inserm, U1106, Marseille, France.,Neurophysiology Departement, La Timone Hospital, Marseille, France
| | - Stephan Chabardès
- Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Philippe Kahane
- Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, Université Grenoble Alpes, Grenoble, France
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Klimes P, Cimbalnik J, Brazdil M, Hall J, Dubeau F, Gotman J, Frauscher B. NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram. Epilepsia 2019; 60:2404-2415. [PMID: 31705527 DOI: 10.1111/epi.16377] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 10/09/2019] [Accepted: 10/09/2019] [Indexed: 01/26/2023]
Abstract
OBJECTIVE Interictal epileptiform anomalies such as epileptiform discharges or high-frequency oscillations show marked variations across the sleep-wake cycle. This study investigates which state of vigilance is the best to localize the epileptogenic zone (EZ) in interictal intracranial electroencephalography (EEG). METHODS Thirty patients with drug-resistant epilepsy undergoing stereo-EEG (SEEG)/sleep recording and subsequent open surgery were included; 13 patients (43.3%) had good surgical outcome (Engel class I). Sleep was scored following standard criteria. Multiple features based on the interictal EEG (interictal epileptiform discharges, high-frequency oscillations, univariate and bivariate features) were used to train a support vector machine (SVM) model to classify SEEG contacts placed in the EZ. The performance of the algorithm was evaluated by the mean area under the receiver-operating characteristic (ROC) curves (AUCs) and positive predictive values (PPVs) across 10-minute sections of wake, non-rapid eye movement sleep (NREM) stages N2 and N3, REM sleep, and their combination. RESULTS Highest AUCs were achieved in NREM sleep stages N2 and N3 compared to wakefulness and REM (P < .01). There was no improvement when using a combination of all four states (P > .05); the best performing features in the combined state were selected from NREM sleep. There were differences between good (Engel I) and poor (Engel II-IV) outcomes in PPV (P < .05) and AUC (P < .01) across all states. The SVM multifeature approach outperformed spikes and high-frequency oscillations (P < .01) and resulted in results similar to those of the seizure-onset zone (SOZ; P > .05). SIGNIFICANCE Sleep improves the localization of the EZ with best identification obtained in NREM sleep stages N2 and N3. Results based on the multifeature classification in 10 minutes of NREM sleep were not different from the results achieved by the SOZ based on 12.7 days of seizure monitoring. This finding might ultimately result in a more time-efficient intracranial presurgical investigation of focal epilepsy.
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Affiliation(s)
- Petr Klimes
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada.,Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic
| | - Jan Cimbalnik
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - Milan Brazdil
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Behavioral and Social Neuroscience Research Group, CEITEC Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Jeffery Hall
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montréal, Quebec, Canada
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Di Giacomo R, Uribe-San-Martin R, Mai R, Francione S, Nobili L, Sartori I, Gozzo F, Pelliccia V, Onofrj M, Lo Russo G, de Curtis M, Tassi L. Stereo-EEG ictal/interictal patterns and underlying pathologies. Seizure 2019; 72:54-60. [DOI: 10.1016/j.seizure.2019.10.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 09/24/2019] [Accepted: 10/01/2019] [Indexed: 11/24/2022] Open
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Li J, Grinenko O, Mosher JC, Gonzalez-Martinez J, Leahy RM, Chauvel P. Learning to define an electrical biomarker of the epileptogenic zone. Hum Brain Mapp 2019; 41:429-441. [PMID: 31609058 PMCID: PMC7268034 DOI: 10.1002/hbm.24813] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 09/17/2019] [Accepted: 09/19/2019] [Indexed: 01/07/2023] Open
Abstract
The role of fast activity as a potential biomarker in localization of the epileptogenic zone (EZ) remains controversial due to recently reported unsatisfactory performance. We recently identified a “fingerprint” of the EZ as a time‐frequency pattern that is defined by a combination of preictal spike(s), fast oscillatory activity, and concurrent suppression of lower frequencies. Here we examine the generalizability of the fingerprint in application to an independent series of patients (11 seizure‐free and 13 non‐seizure‐free after surgery) and show that the fingerprint can also be identified in seizures with lower frequency (such as beta) oscillatory activity. In the seizure‐free group, only 5 of 47 identified EZ contacts were outside the resection. In contrast, in the non‐seizure‐free group, 104 of 142 identified EZ contacts were outside the resection. We integrated the fingerprint prediction with the subject's MR images, thus providing individualized anatomical estimates of the EZ. We show that these fingerprint‐based estimates in seizure‐free patients are almost always inside the resection. On the other hand, for a large fraction of the nonseizure‐free patients the estimated EZ was not well localized and was partially or completely outside the resection, which may explain surgical failure in such cases. We also show that when mapping fast activity alone onto MR images, the EZ was often over‐estimated, indicating a reduced discriminative ability for fast activity relative to the full fingerprint for localization of the EZ.
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Affiliation(s)
- Jian Li
- Signal and Image Processing Institute, University of Southern California, Los Angeles, California
| | - Olesya Grinenko
- Epilepsy Center, Cleveland Clinic Neurological Institute, Cleveland, Ohio
| | - John C Mosher
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas
| | | | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern California, Los Angeles, California
| | - Patrick Chauvel
- Epilepsy Center, Cleveland Clinic Neurological Institute, Cleveland, Ohio
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Multi-feature localization of epileptic foci from interictal, intracranial EEG. Clin Neurophysiol 2019; 130:1945-1953. [PMID: 31465970 DOI: 10.1016/j.clinph.2019.07.024] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 07/09/2019] [Accepted: 07/19/2019] [Indexed: 02/07/2023]
Abstract
OBJECTIVE When considering all patients with focal drug-resistant epilepsy, as high as 40-50% of patients suffer seizure recurrence after surgery. To achieve seizure freedom without side effects, accurate localization of the epileptogenic tissue is crucial before its resection. We investigate an automated, fast, objective mapping process that uses only interictal data. METHODS We propose a novel approach based on multiple iEEG features, which are used to train a support vector machine (SVM) model for classification of iEEG electrodes as normal or pathologic using 30 min of inter-ictal recording. RESULTS The tissue under the iEEG electrodes, classified as epileptogenic, was removed in 17/18 excellent outcome patients and was not entirely resected in 8/10 poor outcome patients. The overall best result was achieved in a subset of 9 excellent outcome patients with the area under the receiver operating curve = 0.95. CONCLUSION SVM models combining multiple iEEG features show better performance than algorithms using a single iEEG marker. Multiple iEEG and connectivity features in presurgical evaluation could improve epileptogenic tissue localization, which may improve surgical outcome and minimize risk of side effects. SIGNIFICANCE In this study, promising results were achieved in localization of epileptogenic regions by SVM models that combine multiple features from 30 min of inter-ictal iEEG recordings.
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Woolfe M, Prime D, Gillinder L, Rowlands D, O'keefe S, Dionisio S. Automatic detection of the epileptogenic zone: An application of the fingerprint of epilepsy. J Neurosci Methods 2019; 325:108347. [PMID: 31330159 DOI: 10.1016/j.jneumeth.2019.108347] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 07/04/2019] [Accepted: 07/04/2019] [Indexed: 11/16/2022]
Abstract
BACKGROUND The successful delineation of the epileptogenic zone in epilepsy monitoring is crucial for achieving seizure freedom after epilepsy surgery. NEW METHOD We aim to improve epileptogenic zone localization by utilizing a computer-assisted tool for the automated grading of the seizure activity recorded in various locations for 20 patients undergoing stereo electroencephalography. Their epileptic seizures were processed to extract two potential biomarkers. The concentration of these biomarkers from within each patient's implantation were then graded to identify their epileptogenic zone and were compared to the clinical assessment. RESULTS Our technique was capable of ranking the clinically defined epileptogenic zone with high accuracy, above 95%, with a true to false positive ratio of 1:1.52, and was effective with both temporal and extra-temporal onset epilepsies. COMPARISON WITH EXISTING METHOD We compared our method to two other groups performing localization using similar biomarkers. Our classification metrics, sensitivity and precision together were comparable to both groups and our overall accuracy from a larger population was also higher then both. CONCLUSIONS Our method is highly accurate, automated and non-parametric providing clinicians another tool that can be used to help identify the epileptogenic zone in patients undergoing the stereo electroencephalography procedure for epilepsy monitoring.
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Affiliation(s)
- Matthew Woolfe
- Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia; School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia.
| | - David Prime
- Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia; School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia
| | - Lisa Gillinder
- Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia
| | - David Rowlands
- School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia
| | - Steven O'keefe
- School of Engineering and Built Environment, Griffith University, Queensland, 4111, Australia
| | - Sasha Dionisio
- Advanced Epilepsy Unit, Mater Adult Hospital Brisbane, Queensland, 4101, Australia
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Lee S, Issa NP, Rose S, Tao JX, Warnke PC, Towle VL, van Drongelen W, Wu S. DC shifts, high frequency oscillations, ripples and fast ripples in relation to the seizure onset zone. Seizure 2019; 77:52-58. [PMID: 31101405 DOI: 10.1016/j.seizure.2019.05.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 01/22/2019] [Accepted: 05/02/2019] [Indexed: 10/26/2022] Open
Abstract
Efforts to improve epilepsy surgery outcomes have led to increased interest in the study of electroencephalographic oscillations outside the conventional EEG bands. These include fast activity above the gamma band, known as high frequency oscillations (HFOs), and infraslow activity (ISA) below the delta band, sometimes referred to as direct current (DC) or ictal baseline shifts (IBS). HFOs in particular have been extensively studied as potential biomarkers for epileptogenic tissue in light of evidence showing that resection of brain tissue containing HFOs is associated with good surgical outcomes. Not all HFOs are conclusively pathological, however, as they can be recorded in nonepileptic tissue and induced by cognitive, visual, or motor tasks. Consequently, efforts to distinguish between pathological and physiological HFOs have identified several traits specific to pathological HFOs, such as coupling with interictal spikes, association with delta waves, and stereotypical morphologies. On the opposite end of the EEG spectrum, sub-delta oscillations have been shown to co-localize with the seizure onset zones (SOZ) and appear in a narrower spatial distribution than activity in the conventional EEG frequency bands. In this report, we review studies that implicate HFOs and ISA in ictogenesis and discuss current limitations such as inter-observer variability and poor standardization of recording techniques. Furthermore, we propose that HFOs and ISA should be analyzed in addition to activity in the conventional EEG band during intracranial presurgical EEG monitoring to identify the best possible surgical margin.
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Affiliation(s)
- Somin Lee
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60607, USA; Committee on Neurobiology, The University of Chicago, Chicago, IL, 60607, USA
| | - Naoum P Issa
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - Sandra Rose
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - James X Tao
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - Peter C Warnke
- Department of Surgery, The University of Chicago, Chicago, IL, 60607, USA
| | - Vernon L Towle
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA; Department of Surgery, The University of Chicago, Chicago, IL, 60607, USA; Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, 60607, USA
| | - Wim van Drongelen
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60607, USA; Committee on Neurobiology, The University of Chicago, Chicago, IL, 60607, USA; Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA; Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, 60607, USA
| | - Shasha Wu
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA.
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Malkov A, Ivanov AI, Latyshkova A, Bregestovski P, Zilberter M, Zilberter Y. Activation of nicotinamide adenine dinucleotide phosphate oxidase is the primary trigger of epileptic seizures in rodent models. Ann Neurol 2019; 85:907-920. [PMID: 30937971 DOI: 10.1002/ana.25474] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Revised: 03/05/2019] [Accepted: 03/31/2019] [Indexed: 12/30/2022]
Abstract
OBJECTIVE Despite decades of epilepsy research, 30% of focal epilepsies remain resistant to antiseizure drugs, with effective drug development impeded by lack of understanding on how seizures are initiated. Here, we report the mechanism of seizure onset relevant to most seizures that are characteristic of focal epilepsies. METHODS Electric and metabolic network parameters were measured using several seizure models in mouse hippocampal slices and acutely induced seizures in rats in vivo to determine metabolic events occurring at seizure onset. RESULTS We show that seizure onset is associated with a rapid release of H2 O2 resulting from N-methyl-D-aspartate (NMDA) receptor-mediated activation of nicotinamide adenine dinucleotide phosphate oxidase (NOX). NOX blockade prevented the fast H2 O2 release as well as the direct current shift and seizurelike event induction in slices. Similarly, intracerebroventricular injection of NOX antagonists prevented acutely induced seizures in rats. INTERPRETATION Our results show that seizures are initiated by NMDA receptor-mediated NOX-induced oxidative stress and can be arrested by NOX inhibition. We introduce a novel use for blood-brain barrier-permeable NOX inhibitor with a significant potential to become the first seizure-specific medication. Thus, targeting NOX may provide a breakthrough treatment for focal epilepsies. ANN NEUROL 2019;85:907-920.
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Affiliation(s)
- Anton Malkov
- Aix-Marseille University, National Institute of Health and Medical Research, Institute of Neuroscience Systems, Mixed Unit of Research 1106, Marseille, France.,Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, Russia
| | - Anton I Ivanov
- Aix-Marseille University, National Institute of Health and Medical Research, Institute of Neuroscience Systems, Mixed Unit of Research 1106, Marseille, France
| | - Alexandra Latyshkova
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, Russia
| | - Piotr Bregestovski
- Aix-Marseille University, National Institute of Health and Medical Research, Institute of Neuroscience Systems, Mixed Unit of Research 1106, Marseille, France.,Institute of Neurosciences, Kazan State Medical University, Kazan, Russia
| | - Misha Zilberter
- Gladstone Institute of Neurological Disease, San Francisco, CA
| | - Yuri Zilberter
- Aix-Marseille University, National Institute of Health and Medical Research, Institute of Neuroscience Systems, Mixed Unit of Research 1106, Marseille, France
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Grinenko O, Li J, Mosher JC, Wang IZ, Bulacio JC, Gonzalez-Martinez J, Nair D, Najm I, Leahy RM, Chauvel P. A fingerprint of the epileptogenic zone in human epilepsies. Brain 2019; 141:117-131. [PMID: 29253102 PMCID: PMC5837527 DOI: 10.1093/brain/awx306] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 09/27/2017] [Indexed: 11/14/2022] Open
Abstract
Defining a bio-electrical marker for the brain area responsible for initiating a seizure remains an unsolved problem. Fast gamma activity has been identified as the most specific marker for seizure onset, but conflicting results have been reported. In this study, we describe an alternative marker, based on an objective description of interictal to ictal transition, with the aim of identifying a time-frequency pattern or ‘fingerprint’ that can differentiate the epileptogenic zone from areas of propagation. Seventeen patients who underwent stereoelectroencephalography were included in the study. Each had seizure onset characterized by sustained gamma activity and were seizure-free after tailored resection or laser ablation. We postulated that the epileptogenic zone was always located inside the resection region based on seizure freedom following surgery. To characterize the ictal frequency pattern, we applied the Morlet wavelet transform to data from each pair of adjacent intracerebral electrode contacts. Based on a visual assessment of the time-frequency plots, we hypothesized that a specific time-frequency pattern in the epileptogenic zone should include a combination of (i) sharp transients or spikes; preceding (ii) multiband fast activity concurrent; with (iii) suppression of lower frequencies. To test this hypothesis, we developed software that automatically extracted each of these features from the time-frequency data. We then used a support vector machine to classify each contact-pair as being within epileptogenic zone or not, based on these features. Our machine learning system identified this pattern in 15 of 17 patients. The total number of identified contacts across all patients was 64, with 58 localized inside the resected area. Subsequent quantitative analysis showed strong correlation between maximum frequency of fast activity and suppression inside the resection but not outside. We did not observe significant discrimination power using only the maximum frequency or the timing of fast activity to differentiate contacts either between resected and non-resected regions or between contacts identified as epileptogenic versus non-epileptogenic. Instead of identifying a single frequency or a single timing trait, we observed the more complex pattern described above that distinguishes the epileptogenic zone. This pattern encompasses interictal to ictal transition and may extend until seizure end. Its time-frequency characteristics can be explained in light of recent models emphasizing the role of fast inhibitory interneurons acting on pyramidal cells as a prominent mechanism in seizure triggering. The pattern clearly differentiates the epileptogenic zone from areas of propagation and, as such, represents an epileptogenic zone ‘fingerprint’.
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Affiliation(s)
- Olesya Grinenko
- Epilepsy Center, Cleveland Clinic Neurological Institute, Cleveland OH, USA
| | - Jian Li
- Signal and Image Processing Institute, University of Southern California, Los Angeles CA, USA
| | - John C Mosher
- Epilepsy Center, Cleveland Clinic Neurological Institute, Cleveland OH, USA
| | - Irene Z Wang
- Epilepsy Center, Cleveland Clinic Neurological Institute, Cleveland OH, USA
| | - Juan C Bulacio
- Epilepsy Center, Cleveland Clinic Neurological Institute, Cleveland OH, USA
| | | | - Dileep Nair
- Epilepsy Center, Cleveland Clinic Neurological Institute, Cleveland OH, USA
| | - Imad Najm
- Epilepsy Center, Cleveland Clinic Neurological Institute, Cleveland OH, USA
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern California, Los Angeles CA, USA
| | - Patrick Chauvel
- Epilepsy Center, Cleveland Clinic Neurological Institute, Cleveland OH, USA
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42
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Chauvel P, Gonzalez-Martinez J, Bulacio J. Presurgical intracranial investigations in epilepsy surgery. HANDBOOK OF CLINICAL NEUROLOGY 2019; 161:45-71. [PMID: 31307620 DOI: 10.1016/b978-0-444-64142-7.00040-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Identification and localization of the "epileptogenic process" in the brain of patients with drug-resistant epilepsy for surgical cure is the goal of presurgical investigations. Intracranial recordings are required when conflicting data between seizure clinical semiology and EEG prevent precise localization within one hemisphere or lateralization, when a visible lesion on MRI seems unrelated to the electroclinical data, or in MRI-negative cases. Two methods are currently used. The objective of the subdural grid electrocorticography with or without depth electrodes (SDG/DE) is the best possible identification of the area of onset of spontaneous seizures and localization of the eloquent cortex. The objective of stereoelectroencephalography (SEEG) is to define the epileptogenic zone (configured as a network) and its relation to an unmasked lesion. Two-dimensional (SDG) and three-dimensional (SEEG) brain sampling dictate different strategies for noninvasive presurgical phase I goals as well as for data analysis. SEEG must resolve several potential localization hypotheses in a manner that cannot be achieved with SDG. SDG operates through brain surface coverage, unlike SEEG, which samples networks. SDG estimates the extent of cortical resection through a lobar or sublobar localization of ictal onset and constraints from functional mapping. SEEG defines a tailored resection according to the results of anatomo-electro-clinical correlations in stereotaxic space that will guide the ablation of the epileptogenic zone. SEEG is currently expanding faster than SDG. The prerequisites (especially in the preimplantation hypothetical strategy) and technical tools (especially stimulation and functional mapping) in the two methods are very different. This chapter presents a comparative review of the rationale, indications, electrode implantation strategies, interpretation, and surgical decision making of these two approaches of presurgical evaluation for epilepsy surgery.
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Affiliation(s)
- Patrick Chauvel
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States.
| | | | - Juan Bulacio
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
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Abstract
PURPOSE It has been challenging to detect early changes preceding seizure onset in patients with epilepsy. This study investigated the preictal discharges (PIDs) by intracranial electroencephalogram of 11 seizures from 7 patients with mesial temporal lobe epilepsy. METHODS The EEG segments consisting of 30 seconds before ictal onset and 5 seconds after ictal onset were selected for analysis. After PID detection, the amplitude and interval were measured. According to the timing of PID onset, the 30-second period preceding seizure onset was divided into two stages: before PID stage and PID stage. The autocorrelation coefficients during the two stages were calculated and compared. RESULTS Preictal discharge amplitude progressively increased, while PID interval gradually decreased toward seizure onset. The autocorrelation coefficients of PID channels were significantly higher during PID stage than before PID stage. There was an overlap between channels with PIDs and seizure onset channels (80.77%). CONCLUSIONS Preictal discharges emerge prior to ictal event, with a dynamic change and a spatial correlation with seizure onset zone. These findings deepen our understanding of seizure generation and help early prediction and localization of seizure onset zone.
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Gnatkovsky V, Pelliccia V, de Curtis M, Tassi L. Two main focal seizure patterns revealed by intracerebral electroencephalographic biomarker analysis. Epilepsia 2018; 60:96-106. [DOI: 10.1111/epi.14610] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 10/08/2018] [Accepted: 10/29/2018] [Indexed: 02/06/2023]
Affiliation(s)
- Vadym Gnatkovsky
- Epilepsy Unit; Institute of Cure, Recovery, and Scientific Research (IRCCS) Foundation Carlo Besta Neurological Institute; Milan Italy
| | | | - Marco de Curtis
- Epilepsy Unit; Institute of Cure, Recovery, and Scientific Research (IRCCS) Foundation Carlo Besta Neurological Institute; Milan Italy
| | - Laura Tassi
- Claudio Munari Epilepsy Surgery Center; Niguarda Hospital; Milan Italy
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Weiss SA, Staba R, Bragin A, Moxon K, Sperling M, Avoli M, Engel J. "Interneurons and principal cell firing in human limbic areas at focal seizure onset". Neurobiol Dis 2018; 124:183-188. [PMID: 30471414 DOI: 10.1016/j.nbd.2018.11.014] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 11/11/2018] [Accepted: 11/19/2018] [Indexed: 10/27/2022] Open
Affiliation(s)
- Shennan A Weiss
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA.
| | - Richard Staba
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Anatol Bragin
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
| | - Karen Moxon
- Dept. of Biomedical Engineering, UC Davis, Davis, CA 95616, USA
| | - Michael Sperling
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Massimo Avoli
- Montreal Neurological Institute, Depts. of Neurology & Neurosurgery and of Physiology, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Jerome Engel
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA; Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA; Dept. of Neurobiology, Dept. of Psychiatry and Biobehavioral Sciences, Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA
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Andrzejak RG, Ruzzene G, Malvestio I, Schindler K, Schöll E, Zakharova A. Mean field phase synchronization between chimera states. CHAOS (WOODBURY, N.Y.) 2018; 28:091101. [PMID: 30278634 DOI: 10.1063/1.5049750] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 08/15/2018] [Indexed: 06/08/2023]
Abstract
We study two-layer networks of identical phase oscillators. Each individual layer is a ring network for which a non-local intra-layer coupling leads to the formation of a chimera state. The number of oscillators and their natural frequencies is in general different across the layers. We couple the phases of individual oscillators in one layer to the phase of the mean field of the other layer. This coupling from the mean field to individual oscillators is done in both directions. For a sufficient strength of this inter-layer coupling, the phases of the mean fields lock across the two layers. In contrast, both layers continue to exhibit chimera states with no locking between the phases of individual oscillators across layers, and the two mean field amplitudes remain uncorrelated. Hence, the networks' mean fields show phase synchronization which is analogous to the one between low-dimensional chaotic oscillators. The required coupling strength to achieve this mean field phase synchronization increases with the mismatches in the network sizes and the oscillators' natural frequencies.
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Affiliation(s)
- Ralph G Andrzejak
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
| | - Giulia Ruzzene
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
| | - Irene Malvestio
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
| | - Kaspar Schindler
- Department of Neurology, Sleep-Wake-Epilepsy-Center, Inselspital, University Hospital, University Bern, Freiburgstrasse 18, 3010 Bern, Switzerland
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstrasse 36, 10623 Berlin, Germany
| | - Anna Zakharova
- Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstrasse 36, 10623 Berlin, Germany
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Murakami H, Wang ZI, Marashly A, Krishnan B, Prayson RA, Kakisaka Y, Mosher JC, Bulacio J, Gonzalez-Martinez JA, Bingaman WE, Najm IM, Burgess RC, Alexopoulos AV. Correlating magnetoencephalography to stereo-electroencephalography in patients undergoing epilepsy surgery. Brain 2018; 139:2935-2947. [PMID: 27567464 DOI: 10.1093/brain/aww215] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 07/06/2016] [Indexed: 11/15/2022] Open
Affiliation(s)
- Hiroatsu Murakami
- Department of Neurosurgery, Brain Research Institute, Niigata University, Niigata, Japan.,Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Zhong I Wang
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Ahmad Marashly
- Department of Child Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Balu Krishnan
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Richard A Prayson
- Department of Anatomic Pathology, Cleveland Clinic, Cleveland, OH, USA
| | - Yosuke Kakisaka
- Department of Epileptology, Tohoku University School of Medicine, Sendai, Japan
| | - John C Mosher
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Juan Bulacio
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | | | | | - Imad M Najm
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
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Potassium dynamics and seizures: Why is potassium ictogenic? Epilepsy Res 2018; 143:50-59. [DOI: 10.1016/j.eplepsyres.2018.04.005] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 03/26/2018] [Accepted: 04/07/2018] [Indexed: 01/01/2023]
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Li A, Chennuri B, Subramanian S, Yaffe R, Gliske S, Stacey W, Norton R, Jordan A, Zaghloul KA, Inati SK, Agrawal S, Haagensen JJ, Hopp J, Atallah C, Johnson E, Crone N, Anderson WS, Fitzgerald Z, Bulacio J, Gale JT, Sarma SV, Gonzalez-Martinez J. Using network analysis to localize the epileptogenic zone from invasive EEG recordings in intractable focal epilepsy. Netw Neurosci 2018; 2:218-240. [PMID: 30215034 PMCID: PMC6130438 DOI: 10.1162/netn_a_00043] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Accepted: 01/09/2018] [Indexed: 01/22/2023] Open
Abstract
Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60–65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ. Epilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often require observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patient’s brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during, and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.
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Affiliation(s)
- Adam Li
- Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Bhaskar Chennuri
- Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sandya Subramanian
- Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Robert Yaffe
- Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | - Austin Jordan
- Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Sara K Inati
- Office of the Clinical Director, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Shubhi Agrawal
- Office of the Clinical Director, National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | | | - Jennifer Hopp
- Neurology, University of Maryland Medical Center, Baltimore, MD, USA
| | - Chalita Atallah
- Neurology, University of Maryland Medical Center, Baltimore, MD, USA
| | - Emily Johnson
- Neurology, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Nathan Crone
- Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, USA
| | | | | | - Juan Bulacio
- Neurosurgery, Cleveland Clinic, Cleveland, OH, USA
| | - John T Gale
- Neurosurgery, Cleveland Clinic, Cleveland, OH, USA
| | - Sridevi V Sarma
- Institute for Computational Medicine, Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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Varatharajah Y, Berry B, Cimbalnik J, Kremen V, Van Gompel J, Stead M, Brinkmann B, Iyer R, Worrell G. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J Neural Eng 2018; 15:046035. [PMID: 29855436 DOI: 10.1088/1741-2552/aac960] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
OBJECTIVE An ability to map seizure-generating brain tissue, i.e. the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity. APPROACH Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy. MAIN RESULTS Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between 90 min and 2 h are sufficient to localize SOZs with accuracies that may prove clinically relevant. SIGNIFICANCE The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. Broadly, our study demonstrates the use of artificial intelligence coupled with careful feature engineering in augmenting clinical decision making.
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Affiliation(s)
- Yogatheesan Varatharajah
- Electrical and Computer Engineering, University of Illinois, Urbana, IL 61801, United States of America
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