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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; 65:2041-2053. [PMID: 38687176 DOI: 10.1111/epi.17996] [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: 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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Weiss SA, Sperling MR, Engel J, Liu A, Fried I, Wu C, Doyle W, Mikell C, Mofakham S, Salamon N, Sim MS, Bragin A, Staba R. Simulated resections and RNS placement can optimize post-operative seizure outcomes when guided by fast ripple networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.26.24304802. [PMID: 38585730 PMCID: PMC10996761 DOI: 10.1101/2024.03.26.24304802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
In medication-resistant epilepsy, the goal of epilepsy surgery is to make a patient seizure free with a resection/ablation that is as small as possible to minimize morbidity. The standard of care in planning the margins of epilepsy surgery involves electroclinical delineation of the seizure onset zone (SOZ) and incorporation of neuroimaging findings from MRI, PET, SPECT, and MEG modalities. Resecting cortical tissue generating high-frequency oscillations (HFOs) has been investigated as a more efficacious alternative to targeting the SOZ. In this study, we used a support vector machine (SVM), with four distinct fast ripple (FR: 350-600 Hz on oscillations, 200-600 Hz on spikes) metrics as factors. These metrics included the FR resection ratio (RR), a spatial FR network measure, and two temporal FR network measures. The SVM was trained by the value of these four factors with respect to the actual resection boundaries and actual seizure free labels of 18 patients with medically refractory focal epilepsy. Leave one out cross-validation of the trained SVM in this training set had an accuracy of 0.78. We next used a simulated iterative virtual resection targeting the FR sites that were highest rate and showed most temporal autonomy. The trained SVM utilized the four virtual FR metrics to predict virtual seizure freedom. In all but one of the nine patients seizure free after surgery, we found that the virtual resections sufficient for virtual seizure freedom were larger in volume (p<0.05). In nine patients who were not seizure free, a larger virtual resection made five virtually seizure free. We also examined 10 medically refractory focal epilepsy patients implanted with the responsive neurostimulator system (RNS) and virtually targeted the RNS stimulation contacts proximal to sites generating FR at highest rates to determine if the simulated value of the stimulated SOZ and stimulated FR metrics would trend toward those patients with a better seizure outcome. Our results suggest: 1) FR measures can accurately predict whether a resection, defined by the standard of care, will result in seizure freedom; 2) utilizing FR alone for planning an efficacious surgery can be associated with larger resections; 3) when FR metrics predict the standard of care resection will fail, amending the boundaries of the planned resection with certain FR generating sites may improve outcome; and 4) more work is required to determine if targeting RNS stimulation contact proximal to FR generating sites will improve seizure outcome.
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Affiliation(s)
- Shennan Aibel Weiss
- Dept. of Neurology, State University of New York Downstate, Brooklyn, New York 11203, USA
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York 11203, USA
- Dept. of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, 11203 USA
| | - Michael R. Sperling
- Dept. of Neurology, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Jerome Engel
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
- Dept. of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
- Dept. of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
| | - Anli Liu
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, 10016 USA
- Neuroscience Institute, NYU Langone Medical Center, New York, NY, 10016 USA
| | - Itzhak Fried
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
| | - Chengyuan Wu
- Dept. of Neuroradiology, Thomas Jefferson University, Philadelphia, PA, 19107, USA
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Werner Doyle
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, 10016 USA
| | - Charles Mikell
- Department of Neurosurgery, State University of New York Stony Brook, Stony Brook, New York 11790, USA
| | - Sima Mofakham
- Department of Neurosurgery, State University of New York Stony Brook, Stony Brook, New York 11790, USA
| | - Noriko Salamon
- Dept. of Neuroradiology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
| | - Myung Shin Sim
- Dept. of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
| | - Anatol Bragin
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
| | - Richard Staba
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
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Jaber K, Avigdor T, Mansilla D, Ho A, Thomas J, Abdallah C, Chabardes S, Hall J, Minotti L, Kahane P, Grova C, Gotman J, Frauscher B. A spatial perturbation framework to validate implantation of the epileptogenic zone. Nat Commun 2024; 15:5253. [PMID: 38897997 PMCID: PMC11187199 DOI: 10.1038/s41467-024-49470-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 06/04/2024] [Indexed: 06/21/2024] Open
Abstract
Stereo-electroencephalography (SEEG) is the gold standard to delineate surgical targets in focal drug-resistant epilepsy. SEEG uses electrodes placed directly into the brain to identify the seizure-onset zone (SOZ). However, its major constraint is limited brain coverage, potentially leading to misidentification of the 'true' SOZ. Here, we propose a framework to assess adequate SEEG sampling by coupling epileptic biomarkers with their spatial distribution and measuring the system's response to a perturbation of this coupling. We demonstrate that the system's response is strongest in well-sampled patients when virtually removing the measured SOZ. We then introduce the spatial perturbation map, a tool that enables qualitative assessment of the implantation coverage. Probability modelling reveals a higher likelihood of well-implanted SOZs in seizure-free patients or non-seizure free patients with incomplete SOZ resections, compared to non-seizure-free patients with complete resections. This highlights the framework's value in sparing patients from unsuccessful surgeries resulting from poor SEEG coverage.
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Affiliation(s)
- Kassem Jaber
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, USA
| | - Tamir Avigdor
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, QC, Canada
| | - Daniel Mansilla
- Neurophysiology Unit, Institute of Neurosurgery Dr. Asenjo, Santiago, Chile
| | - Alyssa Ho
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
- Department of Neurology, Duke University Medical Center, Durham, NC, USA
| | - John Thomas
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, USA
| | - Chifaou Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, QC, Canada
| | - Stephan Chabardes
- Grenoble Institute Neurosciences, Inserm, U1216, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
| | - Jeff Hall
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
| | - Lorella Minotti
- Grenoble Institute Neurosciences, Inserm, U1216, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
| | - Philippe Kahane
- Grenoble Institute Neurosciences, Inserm, U1216, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, QC, Canada
- Multimodal Functional Imaging Lab, School of Health, Department of Physics, Concordia University, Montréal, QC, Canada
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Jean Gotman
- Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada.
- Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, USA.
- Department of Neurology, Duke University Medical Center, Durham, NC, USA.
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Shi H, Pattnaik AR, Aguila C, Lucas A, Sinha N, Prager B, Mojena M, Gallagher R, Parashos A, Bonilha L, Gleichgerrcht E, Davis KA, Litt B, Conrad EC. Utility of intracranial EEG networks depends on re-referencing and connectivity choice. Brain Commun 2024; 6:fcae165. [PMID: 38799618 PMCID: PMC11126314 DOI: 10.1093/braincomms/fcae165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/02/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Šidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.
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Affiliation(s)
- Haoer Shi
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Akash Ranjan Pattnaik
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Carlos Aguila
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alfredo Lucas
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian Prager
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marissa Mojena
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ryan Gallagher
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alexandra Parashos
- Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Leonardo Bonilha
- Department of Neurology, Emory University, Atlanta, GA 30325, USA
| | | | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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5
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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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Sun L, Feng C, Zhang E, Chen H, Jin W, Zhu J, Yu L. High-performance prediction of epilepsy surgical outcomes based on the genetic neural networks and hybrid iEEG marker. Sci Rep 2024; 14:6198. [PMID: 38486013 PMCID: PMC10940588 DOI: 10.1038/s41598-024-56827-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024] Open
Abstract
Accurately identification of the seizure onset zone (SOZ) is pivotal for successful surgery in patients with medically refractory epilepsy. The purpose of this study is to improve the performance of model predicting the epilepsy surgery outcomes using genetic neural network (GNN) model based on a hybrid intracranial electroencephalography (iEEG) marker. We extracted 21 SOZ related markers based on iEEG data from 79 epilepsy patients. The least absolute shrinkage and selection operator (LASSO) regression was employed to integrated seven markers, selected after testing in pairs with all 21 biomarkers and 7 machine learning models, into a hybrid marker. Based on the hybrid marker, we devised a GNN model and compared its predictive performance for surgical outcomes with six other mainstream machine-learning models. Compared to the mainstream models, underpinning the GNN with the hybrid iEEG marker resulted in a better prediction of surgical outcomes, showing a significant increase of the prediction accuracy from approximately 87% to 94.3% (P = 0.0412). This study suggests that the hybrid iEEG marker can improve the performance of model predicting the epilepsy surgical outcomes, and validates the effectiveness of the GNN in characterizing and analyzing complex relationships between clinical data variables.
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Affiliation(s)
- Lipeng Sun
- Second Clinical Medical School, Zhejiang Chinese Medical University, Hangzhou, China
| | - Chen Feng
- Department of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- School of Medicine, Epilepsy Center, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - En Zhang
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Huan Chen
- Department of Physical and Environmental Sciences, University of Toronto, Toronto, Canada
| | - Weifeng Jin
- School of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, China
| | - Junming Zhu
- Department of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China.
- School of Medicine, Epilepsy Center, Second Affiliated Hospital, Zhejiang University, Hangzhou, China.
| | - Li Yu
- School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
- Key Laboratory of Drug Safety Evaluation and Research of Zhejiang Province, Hangzhou Medical College, Hangzhou, China.
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Lin J, Smith GC, Gliske SV, Zochowski M, Shedden K, Stacey WC. High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery. Brain Commun 2024; 6:fcae032. [PMID: 38384998 PMCID: PMC10881100 DOI: 10.1093/braincomms/fcae032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 12/28/2023] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
High frequency oscillations are a promising biomarker of outcome in intractable epilepsy. Prior high frequency oscillation work focused on counting high frequency oscillations on individual channels, and it is still unclear how to translate those results into clinical care. We show that high frequency oscillations arise as network discharges that have valuable properties as predictive biomarkers. Here, we develop a tool to predict patient outcome before surgical resection is performed, based on only prospective information. In addition to determining high frequency oscillation rate on every channel, we performed a correlational analysis to evaluate the functional connectivity of high frequency oscillations in 28 patients with intracranial electrodes. We found that high frequency oscillations were often not solitary events on a single channel, but part of a local network discharge. Eigenvector and outcloseness centrality were used to rank channel importance within the connectivity network, then used to compare patient outcome by comparison with the seizure onset zone or a proportion within the proposed resected channels (critical resection percentage). Combining the knowledge of each patient's seizure onset zone resection plan along with our computed high frequency oscillation network centralities and high frequency oscillation rate, we develop a Naïve Bayes model that predicts outcome (positive predictive value: 100%) better than predicting based upon fully resecting the seizure onset zone (positive predictive value: 71%). Surgical margins had a large effect on outcomes: non-palliative patients in whom most of the seizure onset zone was resected ('definitive surgery', ≥ 80% resected) had predictable outcomes, whereas palliative surgeries (<80% resected) were not predictable. These results suggest that the addition of network properties of high frequency oscillations is more accurate in predicting patient outcome than seizure onset zone alone in patients with most of the seizure onset zone removed and offer great promise for informing clinical decisions in surgery for refractory epilepsy.
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Affiliation(s)
- Jack Lin
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Garnett C Smith
- Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Stephen V Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Michal Zochowski
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Physics and Biophysics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kerby Shedden
- Department of Statistics and Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - William C Stacey
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Division of Neurology, Ann Arbor VA Health System, Ann Arbor, MI 48109, USA
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8
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Lin S, Jiang J, Huang K, Li L, He X, Du P, Wu Y, Liu J, Li X, Huang Z, Zhou Z, Yu Y, Gao J, Lei M, Wu H. Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces. ACS NANO 2023; 17:24487-24513. [PMID: 38064282 DOI: 10.1021/acsnano.3c06781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.
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Affiliation(s)
- Sen Lin
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Jingjing Jiang
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Kai Huang
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Lei Li
- National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
| | - Xian He
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Peng Du
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Yufeng Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Junchen Liu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Xilin Li
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
- Advanced Institute for Brain and Intelligence, Guangxi University, Nanning 530004, China
| | - Zhibao Huang
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Zenan Zhou
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Yuanhang Yu
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Jiaxin Gao
- School of Physical Science and Technology, Guangxi University, Nanning 530004, China
| | - Ming Lei
- State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Hui Wu
- State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
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9
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Yoo S, Kim M, Choi C, Kim DH, Cha GD. Soft Bioelectronics for Neuroengineering: New Horizons in the Treatment of Brain Tumor and Epilepsy. Adv Healthc Mater 2023:e2303563. [PMID: 38117136 DOI: 10.1002/adhm.202303563] [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: 10/17/2023] [Revised: 11/23/2023] [Indexed: 12/21/2023]
Abstract
Soft bioelectronic technologies for neuroengineering have shown remarkable progress, which include novel soft material technologies and device design strategies. Such technological advances that are initiated from fundamental brain science are applied to clinical neuroscience and provided meaningful promises for significant improvement in the diagnosis efficiency and therapeutic efficacy of various brain diseases recently. System-level integration strategies in consideration of specific disease circumstances can enhance treatment effects further. Here, recent advances in soft implantable bioelectronics for neuroengineering, focusing on materials and device designs optimized for the treatment of intracranial disease environments, are reviewed. Various types of soft bioelectronics for neuroengineering are categorized and exemplified first, and then details for the sensing and stimulating device components are explained. Next, application examples of soft implantable bioelectronics to clinical neuroscience, particularly focusing on the treatment of brain tumor and epilepsy are reviewed. Finally, an ideal system of soft intracranial bioelectronics such as closed-loop-type fully-integrated systems is presented, and the remaining challenges for their clinical translation are discussed.
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Affiliation(s)
- Seungwon Yoo
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, 08826, Republic of Korea
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul, 08826, Republic of Korea
| | - Minjeong Kim
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, 08826, Republic of Korea
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul, 08826, Republic of Korea
| | - Changsoon Choi
- Center for Opto-Electronic Materials and Devices, Post-silicon Semiconductor Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Dae-Hyeong Kim
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, 08826, Republic of Korea
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Materials Science and Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Gi Doo Cha
- Department of Systems Biotechnology, Chung-Ang University, Anseong-si, Gyeonggi-do, 17546, Republic of Korea
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10
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Yang Y, Chen D, Wang J, Wang J, Yan Z, Deng Q, Zhang L, Luan G, Wang M, Li T. Dynamic evolution of the anterior cingulate-insula network during seizures. CNS Neurosci Ther 2023; 29:3901-3912. [PMID: 37309272 PMCID: PMC10651990 DOI: 10.1111/cns.14310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 05/28/2023] [Accepted: 05/31/2023] [Indexed: 06/14/2023] Open
Abstract
OBJECTIVES In physiological situations, the anterior cingulate cortex (ACC) and anterior insular cortex (AIC) are prone to coactivation. The functional connectivity and interaction between ACC and AIC in the context of epilepsy remain unclear. This study aimed to investigate the dynamic coupling between these two brain regions during seizures. METHODS Patients who underwent stereoelectroencephalography (SEEG) recording were included in this study. The SEEG data were visually inspected and quantitatively analyzed. The narrowband oscillations and aperiodic components at seizure onset were parameterized. The frequency-specific non-linear correlation analysis was applied to the functional connectivity. The excitation/inhibition ratio (E:I ratio) reflected by the aperiodic slope was performed to evaluate the excitability. RESULTS Twenty patients were included in the study, with 10 diagnosed with anterior cingulate epilepsy and 10 with anterior insular epilepsy. In both types of epilepsy, the correlation coefficient (h2 ) between the ACC and AIC at seizure onset exhibited a significantly higher value than that during interictal and preictal periods (p < 0.05). The direction index (D) showed a significant increase at seizure onset, serving as an indicator for the direction of information flow between these two brain regions with up to 90% accuracy. The E:I ratio increased significantly at seizure onset, with the seizure-onset zone (SOZ) demonstrating a more pronounced increase compared to non-SOZ (p < 0.05). For seizures originating from AIC, the E:I ratio was significantly higher in the AIC than in the ACC (p = 0.0364). CONCLUSIONS In the context of epilepsy, the ACC and AIC are dynamically coupled during seizures. The functional connectivity and excitability exhibit a significant increase at seizure onset. By analyzing connectivity and excitability, the SOZ in ACC and AIC can be identified. The direction index (D) serves as an indicator for the direction of information flow from SOZ to non-SOZ. Notably, the excitability of SOZ changes more significantly than that of non-SOZ.
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Affiliation(s)
- Yujiao Yang
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Dong Chen
- Key Laboratory of Mental HealthInstitute of Psychology, Chinese Academy of SciencesBeijingChina
| | - Jing Wang
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Jie Wang
- Department of ElectrophysiologyCapital Institute of PediatricsBeijingChina
| | - Zhaofen Yan
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Qinqin Deng
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Liping Zhang
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Guoming Luan
- Department of Functional Neurosurgery, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Epilepsy, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
- Beijing Institute for Brain Disorders, Capital Medical UniversityBeijingChina
| | - Mengyang Wang
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Tianfu Li
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Epilepsy, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
- Beijing Institute for Brain Disorders, Capital Medical UniversityBeijingChina
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11
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Ramantani G, Westover MB, Gliske S, Sarnthein J, Sarma S, Wang Y, Baud MO, Stacey WC, Conrad EC. Passive and active markers of cortical excitability in epilepsy. Epilepsia 2023; 64 Suppl 3:S25-S36. [PMID: 36897228 PMCID: PMC10512778 DOI: 10.1111/epi.17578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/07/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.
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Affiliation(s)
- Georgia Ramantani
- Department of Neuropediatrics and Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Data Science, Massachusetts General Hospital McCance Center for Brain Health, Boston, Massachusetts, USA
- Research Affiliate Faculty, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Research Affiliate Faculty, Broad Institute, Cambridge, Massachusetts, USA
| | - Stephen Gliske
- Department of Neurosurgery, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Sridevi Sarma
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle Upon Tyne, UK
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center, NeuroTec, Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | - William C Stacey
- Department of Neurology, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, Michigan, USA
- Division of Neurology, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, Penn Epilepsy Center, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
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12
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Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. EBioMedicine 2023; 97:104848. [PMID: 37898096 PMCID: PMC10630610 DOI: 10.1016/j.ebiom.2023.104848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 10/30/2023] Open
Abstract
BACKGROUND When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. METHODS We retrospectively investigated data from 43 patients (42% female) with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. FINDINGS Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p = 0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. INTERPRETATION Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. FUNDING This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
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Affiliation(s)
- Jonathan J Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia; Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Division of Neurology, Department of Medicine, Queen's University, Kingston, Canada
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
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13
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尹 宁, 贾 哲, 王 乐, 董 宜. [Analysis of neural fragility in epileptic zone based on stereoelectroencephalography]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:837-842. [PMID: 37879911 PMCID: PMC10600434 DOI: 10.7507/1001-5515.202211056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 08/16/2023] [Indexed: 10/27/2023]
Abstract
There are some limitations in the localization of epileptogenic zone commonly used by human eyes to identify abnormal discharges of intracranial electroencephalography in epilepsy. However, at present, the accuracy of the localization of epileptogenic zone by extracting intracranial electroencephalography features needs to be further improved. As a new method using dynamic network model, neural fragility has potential application value in the localization of epileptogenic zone. In this paper, the neural fragility analysis method was used to analyze the stereoelectroencephalography signals of 35 seizures in 20 patients, and then the epileptogenic zone electrodes were classified using the random forest model, and the classification results were compared with the time-frequency characteristics of six different frequency bands extracted by short-time Fourier transform. The results showed that the area under curve (AUC) of epileptic focus electrodes based on time-frequency analysis was 0.870 (delta) to 0.956 (high gamma), and its classification accuracy increased with the increase of frequency band, while the AUC by using neural fragility could reach 0.957. After fusing the neural fragility and the time-frequency characteristics of the γ and high γ band, the AUC could be further increased to 0.969, which was improved on the original basis. This paper verifies the effectiveness of neural fragility in identifying epileptogenic zone, and provides a theoretical reference for its further clinical application.
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Affiliation(s)
- 宁 尹
- 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China
- 河北工业大学 生命科学与健康工程学院 河北省生物电磁与神经工程重点实验室(天津 300130)Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China
- 河北工业大学 生命科学与健康工程学院 天津市生物电工与智能健康重点实验室(天津 300130)Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China
| | - 哲沛 贾
- 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China
- 河北工业大学 生命科学与健康工程学院 河北省生物电磁与神经工程重点实验室(天津 300130)Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China
- 河北工业大学 生命科学与健康工程学院 天津市生物电工与智能健康重点实验室(天津 300130)Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China
| | - 乐 王
- 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China
| | - 宜林 董
- 河北工业大学 省部共建电工装备可靠性与智能化国家重点实验室(天津 300130)State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China
- 河北工业大学 生命科学与健康工程学院 河北省生物电磁与神经工程重点实验室(天津 300130)Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China
- 河北工业大学 生命科学与健康工程学院 天津市生物电工与智能健康重点实验室(天津 300130)Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China
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14
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Horsley JJ, Thomas RH, Chowdhury FA, Diehl B, McEvoy AW, Miserocchi A, de Tisi J, Vos SB, Walker MC, Winston GP, Duncan JS, Wang Y, Taylor PN. Complementary structural and functional abnormalities to localise epileptogenic tissue. ARXIV 2023:arXiv:2304.03192v3. [PMID: 37064531 PMCID: PMC10104180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Background When investigating suitability for epilepsy surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation of the epileptogenic zone (EZ), improving surgical outcomes in epilepsy. Methods We retrospectively investigated data from 43 patients with epilepsy who had surgery following iEEG. Twenty-five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls. We explored whether the resection of maximal abnormalities related to improved surgical outcomes, in both modalities individually and concurrently. Additionally, we suggest how connectivity abnormalities may inform the placement of iEEG electrodes pre-surgically using a patient case study. Findings Seizure freedom was 15 times more likely in patients with resection of maximal connectivity and iEEG abnormalities (p=0.008). Both modalities separately distinguished patient surgical outcome groups and when used simultaneously, a decision tree correctly separated 36 of 43 (84%) patients. Interpretation Our results suggest that both connectivity and iEEG abnormalities may localise epileptogenic tissue, and that these two modalities may provide complementary information in pre-surgical evaluations. Funding This research was funded by UKRI, CDT in Cloud Computing for Big Data, NIH, MRC, Wellcome Trust and Epilepsy Research UK.
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Affiliation(s)
- Jonathan J. Horsley
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Rhys H. Thomas
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Fahmida A. Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Andrew W. McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B. Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
- Centre for Medical Image Computing, Computer Science Department, University College London, London, United Kingdom
| | - Matthew C. Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Gavin P. Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Division of Neurology, Department of Medicine, Queen’s University, Kingston, Canada
| | - John S. Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Peter N. Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
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15
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Li Z, Zhang H, Niu S, Xing Y. Localizing epileptogenic zones with high-frequency oscillations and directed connectivity. Seizure 2023; 111:9-16. [PMID: 37487273 DOI: 10.1016/j.seizure.2023.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/26/2023] Open
Abstract
PURPOSE Precise localization of the epileptogenic zone (EZ) is essential for epilepsy surgery. Existing methods often fail to detect slow onset patterns or similar neural activities presented in the recorded signals. To address this issue, we propose a new measure to quantify epileptogenicity, i.e., the connectivity high-frequency epileptogenicity index (cHFEI). METHODS The cHFEI method combines directed connectivity and high-frequency oscillations (HFOs) to measure the epileptogenicity of regions involved in a brain network. By applying this method to stereoelectroencephalography (SEEG) recordings of 49 seizures in 20 patients, we calculated the accuracy, sensitivity, and precision with a visually identified epileptogenic zone as a reference. The performance was evaluated by the confusion matrix and the area under the receiver operating characteristic (ROC) curve. RESULTS Epileptic network estimation based on cHFEI successfully distinguished brain regions involved in seizure onset from the propagation network. Moreover, cHFEI outperformed other existing detection methods in the estimation of EZs in all patients, with an average area under the ROC curve of 0.88 and an accuracy of 0.85. CONCLUSIONS cHFEI can characterize EZ in a robust manner despite various seizure onset patterns and has potential application in epilepsy therapy.
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Affiliation(s)
- Zhaohui Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of information transmission and signal processing, Yanshan University, Qinhuangdao 066004, China.
| | - Hao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Shipeng Niu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Yanyu Xing
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
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16
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Sinha N, Duncan JS, Diehl B, Chowdhury FA, de Tisi J, Miserocchi A, McEvoy AW, Davis KA, Vos SB, Winston GP, Wang Y, Taylor PN. Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery. Neurology 2023; 101:e1293-e1306. [PMID: 37652703 PMCID: PMC10558161 DOI: 10.1212/wnl.0000000000207661] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 06/02/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Surgery is an effective treatment for drug-resistant epilepsy, which modifies the brain's structure and networks to regulate seizure activity. Our objective was to examine the relationship between brain structure and function to determine the extent to which this relationship affects the success of the surgery in controlling seizures. We hypothesized that a stronger association between brain structure and function would lead to improved seizure control after surgery. METHODS We constructed functional and structural brain networks in patients with drug-resistant focal epilepsy by using presurgery functional data from intracranial EEG (iEEG) recordings, presurgery and postsurgery structural data from T1-weighted MRI, and presurgery diffusion-weighted MRI. We quantified the relationship (coupling) between structural and functional connectivity by using the Spearman rank correlation and analyzed this structure-function coupling at 2 spatial scales: (1) global iEEG network level and (2) individual iEEG electrode contacts using virtual surgeries. We retrospectively predicted postoperative seizure freedom by incorporating the structure-function connectivity coupling metrics and routine clinical variables into a cross-validated predictive model. RESULTS We conducted a retrospective analysis on data from 39 patients who met our inclusion criteria. Brain areas implanted with iEEG electrodes had stronger structure-function coupling in seizure-free patients compared with those with seizure recurrence (p = 0.002, d = 0.76, area under the receiver operating characteristic curve [AUC] = 0.78 [95% CI 0.62-0.93]). Virtual surgeries on brain areas that resulted in stronger structure-function coupling of the remaining network were associated with seizure-free outcomes (p = 0.007, d = 0.96, AUC = 0.73 [95% CI 0.58-0.89]). The combination of global and local structure-function coupling measures accurately predicted seizure outcomes with a cross-validated AUC of 0.81 (95% CI 0.67-0.94). These measures were complementary to other clinical variables and, when included for prediction, resulted in a cross-validated AUC of 0.91 (95% CI 0.82-1.0), accuracy of 92%, sensitivity of 93%, and specificity of 91%. DISCUSSION Our study showed that the strength of structure-function connectivity coupling may play a crucial role in determining the success of epilepsy surgery. By quantitatively incorporating structure-function coupling measures and standard-of-care clinical variables into presurgical evaluations, we may be able to better localize epileptogenic tissue and select patients for epilepsy surgery. CLASSIFICATION OF EVIDENCE This is a Class IV retrospective case series showing that structure-function mapping may help determine the outcome from surgical resection for treatment-resistant focal epilepsy.
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Affiliation(s)
- Nishant Sinha
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada.
| | - John S Duncan
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Beate Diehl
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Fahmida A Chowdhury
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Jane de Tisi
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Anna Miserocchi
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Andrew William McEvoy
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Kathryn A Davis
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Sjoerd B Vos
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Gavin P Winston
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Yujiang Wang
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
| | - Peter Neal Taylor
- From the Department of Neurology (N.S., K.A.D.), Penn Epilepsy Center, Perelman School of Medicine, and Center for Neuroengineering and Therapeutics (N.S., K.A.D.), University of Pennsylvania, Philadelphia; Translational and Clinical Research Institute (Y.W., P.N.T.), Faculty of Medical Sciences, and Computational Neuroscience, Neurology, and Psychiatry Lab (Y.W., P.N.T.), ICOS Group, School of Computing, Newcastle University; Department of Epilepsy (J.S.D., B.D., F.A.C., J.d.T., A.M., A.W.M., G.P.W., Y.W., P.N.T.), UCL Queen Square Institute of Neurology; UCL Centre for Medical Image Computing (S.B.V.); Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (J.S.D., G.P.W.), Chalfont Centre for Epilepsy, Bucks, United Kingdom; Centre for Microscopy, Characterisation, and Analysis (S.B.V.), The University of Western Australia, Nedlands; and Division of Neurology (G.P.W.), Department of Medicine, Queen's University, Kingston, Canada
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Graham RT, Parrish RR, Alberio L, Johnson EL, Owens L, Trevelyan AJ. Optogenetic stimulation reveals a latent tipping point in cortical networks during ictogenesis. Brain 2023; 146:2814-2827. [PMID: 36572952 PMCID: PMC10316782 DOI: 10.1093/brain/awac487] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/17/2022] [Accepted: 12/06/2022] [Indexed: 12/28/2022] Open
Abstract
Brain-state transitions are readily apparent from changes in brain rhythms,1 but are difficult to predict, suggestive that the underlying cause is latent to passive recording methods. Among the most important transitions, clinically, are the starts of seizures. We here show that an 'active probing' approach may have several important benefits for epileptic management, including by helping predict these transitions. We used mice expressing the optogenetic actuator, channelrhodopsin, in pyramidal cells, allowing this population to be stimulated in isolation. Intermittent stimulation at frequencies as low as 0.033 Hz (period = 30 s) delayed the onset of seizure-like events in an acute brain slice model of ictogenesis, but the effect was lost if stimulation was delivered at even lower frequencies (1/min). Notably, active probing additionally provides advance indication of when seizure-like activity is imminent, revealed by monitoring the postsynaptic response to stimulation. The postsynaptic response, recorded extracellularly, showed an all-or-nothing change in both amplitude and duration, a few hundred seconds before seizure-like activity began-a sufficient length of time to provide a helpful warning of an impending seizure. The change in the postsynaptic response then persisted for the remainder of the recording, indicative of a state change from a pre-epileptic to a pro-epileptic network. This occurred in parallel with a large increase in the stimulation-triggered Ca2+ entry into pyramidal dendrites, and a step increase in the number of evoked postsynaptic action potentials, both consistent with a reduction in the threshold for dendritic action potentials. In 0 Mg2+ bathing media, the reduced threshold was not associated with changes in glutamatergic synaptic function, nor of GABAergic release from either parvalbumin or somatostatin interneurons, but simulations indicate that the step change in the optogenetic response can instead arise from incremental increases in intracellular [Cl-]. The change in the response to stimulation was replicated by artificially raising intracellular [Cl-], using the optogenetic chloride pump, halorhodopsin. By contrast, increases in extracellular [K+] cannot account for the firing patterns in the response to stimulation, although this, and other cellular changes, may contribute to ictal initiation in other circumstances. We describe how these various cellular changes form a synergistic network of positive feedback mechanisms, which may explain the precipitous nature of seizure onset. This model of seizure initiation draws together several major lines of epilepsy research as well as providing an important proof-of-principle regarding the utility of open-loop brain stimulation for clinical management of the condition.
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Affiliation(s)
- Robert T Graham
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - R Ryley Parrish
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Laura Alberio
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Emily L Johnson
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Laura Owens
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
| | - Andrew J Trevelyan
- Medical School, Newcastle University Biosciences Institute, Framlington Place, Newcastle upon Tyne NE2 4HH, UK
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18
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Ye H, He C, Hu W, Xiong K, Hu L, Chen C, Xu S, Xu C, Wang Y, Ding Y, Wu Y, Zhang K, Wang S, Wang S. Pre-ictal fluctuation of EEG functional connectivity discriminates seizure phenotypes in mesial temporal lobe epilepsy. Clin Neurophysiol 2023; 151:107-115. [PMID: 37245497 DOI: 10.1016/j.clinph.2023.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/29/2023] [Accepted: 05/10/2023] [Indexed: 05/30/2023]
Abstract
OBJECTIVE We explored whether quantifiable differences between clinical seizures (CSs) and subclinical seizures (SCSs) occur in the pre-ictal state. METHODS We analyzed pre-ictal stereo-electroencephalography (SEEG) retrospectively across mesial temporal lobe epilepsy patients with recorded CSs and SCSs. Power spectral density and functional connectivity (FC) were quantified within and between the seizure onset zone (SOZ) and the early propagation zone (PZ), respectively. To evaluate the fluctuation of neural connectivity, FC variability was computed. Measures were further verified by a logistic regression model to evaluate their classification potentiality through the area under the receiver-operating-characteristics curve (AUC). RESULTS Fifty-four pre-ictal SEEG epochs (27 CSs and 27 SCSs) were selected among 14 patients. Within the SOZ, pre-ictal FC variability of CSs was larger than SCSs in 1-45 Hz during 30 seconds before seizure onset. Pre-ictal FC variability between the SOZ and PZ was larger in SCSs than CSs in 55-80 Hz within 1 minute before onset. Using these two variables, the logistic regression model achieved an AUC of 0.79 when classifying CSs and SCSs. CONCLUSIONS Pre-ictal FC variability within/between epileptic zones, not signal power or FC value, distinguished SCSs from CSs. SIGNIFICANCE Pre-ictal epileptic network stability possibly marks seizure phenotypes, contributing insights into ictogenesis and potentially helping seizure prediction.
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Affiliation(s)
- Hongyi Ye
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chenmin He
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kai Xiong
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
| | - Lingli Hu
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Cong Chen
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Sha Xu
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Cenglin Xu
- Department of Pharmacology, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Zhejiang Province Key Laboratory of Neurobiology, Basic Medical College, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yi Wang
- Department of Pharmacology, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Zhejiang Province Key Laboratory of Neurobiology, Basic Medical College, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yao Ding
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yingcai Wu
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shan Wang
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
| | - Shuang Wang
- Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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Haderlein JF, Peterson ADH, Burkitt AN, Mareels IMY, Grayden DB. Autoregressive models for biomedical signal processing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083705 DOI: 10.1109/embc40787.2023.10340714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially, this data is subject to measurement errors as well as uncertainties in the underlying system model. As a result, standard signal processing using autoregressive model estimators may be biased. We present a framework for autoregressive modelling that incorporates these uncertainties explicitly via an overparameterised loss function. To optimise this loss, we derive an algorithm that alternates between state and parameter estimation. Our work shows that the procedure is able to successfully denoise time series and successfully reconstruct system parameters.Clinical relevance- This new paradigm can be used in a multitude of applications in neuroscience such as brain-computer interface data analysis and better understanding of brain dynamics in diseases such as epilepsy.
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20
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Bernabei JM, Li A, Revell AY, Smith RJ, Gunnarsdottir KM, Ong IZ, Davis KA, Sinha N, Sarma S, Litt B. Quantitative approaches to guide epilepsy surgery from intracranial EEG. Brain 2023; 146:2248-2258. [PMID: 36623936 PMCID: PMC10232272 DOI: 10.1093/brain/awad007] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/11/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.
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Affiliation(s)
- John M Bernabei
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Li
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Andrew Y Revell
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rachel J Smith
- Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Neuroengineering Program, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Kristin M Gunnarsdottir
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ian Z Ong
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sridevi Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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21
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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: 19] [Impact Index Per Article: 19.0] [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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22
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Peh WY, Thangavel P, Yao Y, Thomas J, Tan YL, Dauwels J. Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG. Int J Neural Syst 2023; 33:2350012. [PMID: 36809996 DOI: 10.1142/s0129065723500120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse characteristics across patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG). First, we deploy a convolutional neural network with transformers and belief matching loss to detect seizures in single-channel EEG segments. Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments. At last, we apply post-processing filters to the segment-level outputs to determine seizures' start and end points in multi-channel EEGs. Finally, we introduce the minimum overlap evaluation scoring as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) dataset and evaluated it on five independent EEG datasets. We evaluate the systems with the following metrics: sensitivity (SEN), precision (PRE), and average and median false positive rate per hour (aFPR/h and mFPR/h). Across four adult scalp EEG and iEEG datasets, we obtained SEN of 0.617-1.00, PRE of 0.534-1.00, aFPR/h of 0.425-2.002, and mFPR/h of 0-1.003. The proposed seizure detector can detect seizures in adult EEGs and takes less than 15[Formula: see text]s for a 30[Formula: see text]min EEG. Hence, this system could aid clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment.
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Affiliation(s)
- Wei Yan Peh
- Interdisciplinary Graduate School (IGS), Nanyang Technological University, Singapore 639798, Singapore
| | - Prasanth Thangavel
- Interdisciplinary Graduate School (IGS), Nanyang Technological University, Singapore 639798, Singapore
| | - Yuanyuan Yao
- Katholieke Universiteit Leuven, Oude Markt 13, 3000 Leuven, Belgium
| | - John Thomas
- Montreal Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada
| | - Yee-Leng Tan
- National Neuroscience Institute, Singapore 308433, Singapore
| | - Justin Dauwels
- Department of Microelectronics, Delft, University of Technology, 2628 CD Delft, Netherlands
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23
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Miron G, Müller PM, Holtkamp M, Meisel C. Prediction of epilepsy surgery outcome using foramen ovale EEG - A machine learning approach. Epilepsy Res 2023; 191:107111. [PMID: 36857943 DOI: 10.1016/j.eplepsyres.2023.107111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/20/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
INTRODUCTION Patients with drug-resistant focal epilepsy may benefit from ablative or resective surgery. In presurgical work-up, intracranial EEG markers have been shown to be useful in identification of the seizure onset zone and prediction of post-surgical seizure freedom. However, in most cases, implantation of depth or subdural electrodes is performed, exposing patients to increased risks of complications. METHODS We analysed EEG data recorded from a minimally invasive approach utilizing foramen ovale (FO) and epidural peg electrodes using a supervised machine learning approach to predict post-surgical seizure freedom. Power-spectral EEG features were incorporated in a logistic regression model predicting one-year post-surgical seizure freedom. The prediction model was validated using repeated 5-fold cross-validation and compared to outcome prediction based on clinical and scalp EEG variables. RESULTS Forty-seven patients (26 patients with post-surgical 1-year seizure freedom) were included in the study, with 31 having FO and 27 patients having peg onset seizures. The area under the receiver-operating curve for post-surgical seizure freedom (Engel 1A) prediction in patients with FO onset seizures was 0.74 ± 0.23 using electrophysiology features, compared to 0.66 ± 0.22 for predictions based on clinical and scalp EEG variables (p < 0.001). The most important features for prediction were spectral power in the gamma and high gamma ranges. EEG data from peg electrodes was not informative in predicting post-surgical outcomes. CONCLUSION In this hypothesis-generating study, a data-driven approach based on EEG features derived from FO electrodes recordings outperformed the predictive ability based solely on clinical and scalp EEG variables. Pending validation in future studies, this method may provide valuable post-surgical prognostic information while minimizing risks of more invasive diagnostic approaches.
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Affiliation(s)
- Gadi Miron
- Epilepsy-Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany; Epilepsy-Center Berlin-Brandenburg, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
| | - Paul Manuel Müller
- Computational Neurology, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany
| | - Martin Holtkamp
- Epilepsy-Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany; Epilepsy-Center Berlin-Brandenburg, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Meisel
- Computational Neurology, Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany; Center for Stroke Research Berlin, Berlin, Germany; NeuroCure Cluster of Excellence, Charité - Universitätsmedizin Berlin, Berlin, Germany
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24
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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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25
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Weiss SA, Fried I, Wu C, Sharan A, Rubinstein D, Engel J, Sperling MR, Staba RJ. Graph theoretical measures of fast ripple networks improve the accuracy of post-operative seizure outcome prediction. Sci Rep 2023; 13:367. [PMID: 36611059 PMCID: PMC9825369 DOI: 10.1038/s41598-022-27248-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 12/28/2022] [Indexed: 01/09/2023] Open
Abstract
Fast ripples (FR) are a biomarker of epileptogenic brain, but when larger portions of FR generating regions are resected seizure freedom is not always achieved. To evaluate and improve the diagnostic accuracy of FR resection for predicting seizure freedom we compared the FR resection ratio (RR) with FR network graph theoretical measures. In 23 patients FR were semi-automatically detected and quantified in stereo EEG recordings during sleep. MRI normalization and co-registration localized contacts and relation to resection margins. The number of FR, and graph theoretical measures, which were spatial (i.e., FR rate-distance radius) or temporal correlational (i.e., FR mutual information), were compared with the resection margins and with seizure outcome We found that the FR RR did not correlate with seizure-outcome (p > 0.05). In contrast, the FR rate-distance radius resected difference and the FR MI mean characteristic path length RR did correlate with seizure-outcome (p < 0.05). Retesting of positive FR RR patients using either FR rate-distance radius resected difference or the FR MI mean characteristic path length RR reduced seizure-free misclassifications from 44 to 22% and 17%, respectively. These results indicate that graph theoretical measures of FR networks can improve the diagnostic accuracy of the resection of FR events for predicting seizure freedom.
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Affiliation(s)
- Shennan A. Weiss
- grid.262863.b0000 0001 0693 2202Department of Neurology, State University of New York Downstate, Brooklyn, USA ,grid.262863.b0000 0001 0693 2202Department of Physiology and Pharmacology, State University of New York Downstate, 450 Clarkson Avenue, MSC 1213, Brooklyn, NY 11203 USA ,grid.422616.50000 0004 0443 7226Department of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY USA
| | - Itzhak Fried
- grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Chengyuan Wu
- grid.265008.90000 0001 2166 5843Department of Neuroradiology, Thomas Jefferson University, Philadelphia, USA ,grid.265008.90000 0001 2166 5843Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107 USA
| | - Ashwini Sharan
- grid.265008.90000 0001 2166 5843Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA 19107 USA
| | - Daniel Rubinstein
- grid.265008.90000 0001 2166 5843Department of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, USA
| | - Jerome Engel
- grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, USA ,grid.19006.3e0000 0000 9632 6718David Geffen School of Medicine at UCLA, Brain Research Institute, Los Angeles, CA 90095 USA
| | - Michael R. Sperling
- grid.265008.90000 0001 2166 5843Department of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, USA
| | - Richard J. Staba
- grid.19006.3e0000 0000 9632 6718Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, USA
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26
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Trevelyan AJ, Graham RT, Parrish RR, Codadu NK. Synergistic Positive Feedback Mechanisms Underlying Seizure Initiation. Epilepsy Curr 2023; 23:38-43. [PMID: 36923333 PMCID: PMC10009126 DOI: 10.1177/15357597221127163] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Investigations into seizure initiation, in recent years, have focused almost entirely upon alterations of interneuronal function, chloride homeostasis, and extracellular potassium levels. In contrast, little attention has been directed toward a possible role of dendritic plateau potentials in the actual ictogenic transition, despite a substantial literature dating back 40 years regarding its importance generally in epilepsy. Here, we argue that an increase in dendritic excitability, coordinated across the population of pyramidal cells, is a key stage in ictogenesis.
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Affiliation(s)
- Andrew J. Trevelyan
- Newcastle University Biosciences Institute, Medical School, Framlington Place, Newcastle upon Tyne, United Kingdom
| | - Robert T. Graham
- Queen Square Institute of Neurology, University College London, United Kingdom
| | - R. Ryley Parrish
- Department of Cell Biology and Physiology, Brigham Young University, Provo, UT, USA
| | - Neela K. Codadu
- Queen Square Institute of Neurology, University College London, United Kingdom
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27
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Hays MA, Smith RJ, Wang Y, Coogan C, Sarma SV, Crone NE, Kang JY. Cortico-cortical evoked potentials in response to varying stimulation intensity improves seizure localization. Clin Neurophysiol 2023; 145:119-128. [PMID: 36127246 PMCID: PMC9771930 DOI: 10.1016/j.clinph.2022.08.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 08/05/2022] [Accepted: 08/27/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVE As single pulse electrical stimulation (SPES) is increasingly utilized to help localize the seizure onset zone (SOZ), it is important to understand how stimulation intensity can affect the ability to use cortico-cortical evoked potentials (CCEPs) to delineate epileptogenic regions. METHODS We studied 15 drug-resistant epilepsy patients undergoing intracranial EEG monitoring and SPES with titrations of stimulation intensity. The N1 amplitude and distribution of CCEPs elicited in the SOZ and non-seizure onset zone (nSOZ) were quantified at each intensity. The separability of the SOZ and nSOZ using N1 amplitudes was compared between models using responses to titrations, responses to one maximal intensity, or both. RESULTS At 2 mA and above, the increase in N1 amplitude with current intensity was greater for responses within the SOZ, and SOZ response distribution was maximized by 4-6 mA. Models incorporating titrations achieved better separability of SOZ and nSOZ compared to those using one maximal intensity. CONCLUSIONS We demonstrated that differences in CCEP amplitude over a range of current intensities can improve discriminability of SOZ regions. SIGNIFICANCE This study provides insight into the underlying excitability of the SOZ and how differences in current-dependent amplitudes of CCEPs may be used to help localize epileptogenic sites.
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Affiliation(s)
- Mark A Hays
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Rachel J Smith
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Yujing Wang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christopher Coogan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sridevi V Sarma
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Nathan E Crone
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joon Y Kang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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28
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Wei PH, Nicolelis MA, Zhao GG. Rethinking the neurosurgical approach to brain disorders from the network neuroscience perspective. Sci Bull (Beijing) 2022; 67:2376-2380. [PMID: 36566053 DOI: 10.1016/j.scib.2022.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Peng-Hu Wei
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Lab of Walk Again Project (WAP), Translational Research Center for Brain-inspired Intelligence, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; National Center for Neurological Disorders, Beijing 100053, China
| | - Miguel A Nicolelis
- Department of Neurobiology, Duke University, Durham 27708, USA; International Institute for Neurosciences of Natal - Edmond and Lily Safra, Nata 59280-000, Brazil
| | - Guo-Guang Zhao
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Lab of Walk Again Project (WAP), Translational Research Center for Brain-inspired Intelligence, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; National Center for Neurological Disorders, Beijing 100053, China; Beijing Municipal Geriatric Medical Research Center, Beijing 100053, China.
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29
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Gunnarsdottir KM, Li A, Smith RJ, Kang JY, Korzeniewska A, Crone NE, Rouse AG, Cheng JJ, Kinsman MJ, Landazuri P, Uysal U, Ulloa CM, Cameron N, Cajigas I, Jagid J, Kanner A, Elarjani T, Bicchi MM, Inati S, Zaghloul KA, Boerwinkle VL, Wyckoff S, Barot N, Gonzalez-Martinez J, Sarma SV. Source-sink connectivity: a novel interictal EEG marker for seizure localization. Brain 2022; 145:3901-3915. [PMID: 36412516 PMCID: PMC10200292 DOI: 10.1093/brain/awac300] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 07/05/2022] [Accepted: 08/01/2022] [Indexed: 07/26/2023] Open
Abstract
Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.
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Affiliation(s)
| | - Adam Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Rachel J Smith
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Joon-Yi Kang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Anna Korzeniewska
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Nathan E Crone
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Adam G Rouse
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Jennifer J Cheng
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Michael J Kinsman
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Patrick Landazuri
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Utku Uysal
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Carol M Ulloa
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Nathaniel Cameron
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Iahn Cajigas
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Jonathan Jagid
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Andres Kanner
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Turki Elarjani
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Manuel Melo Bicchi
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sara Inati
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Varina L Boerwinkle
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Sarah Wyckoff
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Niravkumar Barot
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | | | - Sridevi V Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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30
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Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity. Sci Rep 2022; 12:18998. [PMID: 36348082 PMCID: PMC9643358 DOI: 10.1038/s41598-022-23656-1] [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: 08/15/2022] [Accepted: 11/03/2022] [Indexed: 11/09/2022] Open
Abstract
Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp electroencephalogram (EEG) signals recorded from various regions of a patient's scalp. Brain functional connectivity graphs are generated for the extraction of spatial-temporal resolution of various onset epilepsy seizure patterns. Our supervised GGN model was substantiated by seizure detection and classification experiments. We train the GGN model using a clinically proven dataset of over 3047 epileptic seizure cases. The GGN model achieved a 91% accuracy in classifying seven types of epileptic seizure attacks, which outperformed the 65%, 74%, and 82% accuracy in using the convolutional neural network (CNN), graph neural networks (GNN), and transformer models, respectively. We present the GGN model architecture and operational steps to assist neuroscientists or brain specialists in using dynamic functional connectivity information to detect neurological disorders. Furthermore, we suggest to merge our spatial-temporal graph generator design in upgrading the conventional CNN and GNN models with dynamic convolutional kernels for accuracy enhancement.
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31
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Piper RJ, Richardson RM, Worrell G, Carmichael DW, Baldeweg T, Litt B, Denison T, Tisdall MM. Towards network-guided neuromodulation for epilepsy. Brain 2022; 145:3347-3362. [PMID: 35771657 PMCID: PMC9586548 DOI: 10.1093/brain/awac234] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/30/2022] [Accepted: 06/16/2022] [Indexed: 11/30/2022] Open
Abstract
Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal-onset epilepsy; and there is emerging evidence for their efficacy in children and generalized-onset seizure disorders. The convergence of these advancing fields is driving an era of 'network-guided neuromodulation' for epilepsy. In this review, we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key 'propagation points' in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (i) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (ii) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that have revealed structural and functional networks associated with these propagation points - including scalp and invasive EEG, and diffusion and functional MRI. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neuromodulation for epilepsy to accelerate progress towards two translational goals: (i) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (ii) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizure propagation through mapping and modulation of each patients' individual epileptogenic networks.
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Affiliation(s)
- Rory J Piper
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | | | | | - Torsten Baldeweg
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Brian Litt
- Department of Neurology and Bioengineering, University of Pennsylvania, Philadelphia, USA
| | | | - Martin M Tisdall
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
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32
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Gong Y, Xu C, Wang S, Wang Y, Chen Z. Computerized application for epilepsy in China: Does the era of artificial intelligence comes? Acta Neurol Scand 2022; 146:732-742. [PMID: 36156212 DOI: 10.1111/ane.13711] [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: 07/31/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 12/01/2022]
Abstract
Epilepsy, one of the most common neurological diseases in China, is notorious for its spontaneous, unprovoked and recurrent seizures. The etiology of epilepsy varies among individual patients, including congenital gene mutation, traumatic injury, infections, etc. This heterogeneity partly hampered the accurate diagnosis and choice of appropriate treatments. Encouragingly, great achievements have been achieved in computational science, making it become a key player in medical fields gradually and bringing new hope for rapid and accurate diagnosis as well as targeted therapies in epilepsy. Here, we historically review the advances of computerized applications in epilepsy-especially those tremendous findings achieved in China-for different purposes including seizure prediction, localization of epileptogenic zone, post-surgical prognosis, etc. Special attentions are paid to the great progress based on artificial intelligence (AI), which is more "sensitive", "smart" and "in-depth" than human capacities. At last, we give a comprehensive discussion about the disadvantages and limitations of current computerized applications for epilepsy and propose some future directions as further stepping stones to embrace "the era of AI" in epilepsy.
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Affiliation(s)
- Yiwei Gong
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Cenglin Xu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuang Wang
- Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yi Wang
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
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33
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Schlafly ED, Marshall FA, Merricks EM, Eden UT, Cash SS, Schevon CA, Kramer MA. Multiple Sources of Fast Traveling Waves during Human Seizures: Resolving a Controversy. J Neurosci 2022; 42:6966-6982. [PMID: 35906069 PMCID: PMC9464018 DOI: 10.1523/jneurosci.0338-22.2022] [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: 02/16/2022] [Revised: 05/26/2022] [Accepted: 06/18/2022] [Indexed: 11/21/2022] Open
Abstract
During human seizures, organized waves of voltage activity rapidly sweep across the cortex. Two contradictory theories describe the source of these fast traveling waves: either a slowly advancing narrow region of multiunit activity (an ictal wavefront) or a fixed cortical location. Limited observations and different analyses prevent resolution of these incompatible theories. Here we address this disagreement by combining the methods and microelectrode array recordings (N = 11 patients, 2 females, N = 31 seizures) from previous human studies to analyze the traveling wave source. We find, inconsistent with both existing theories, a transient relationship between the ictal wavefront and traveling waves, and multiple stable directions of traveling waves in many seizures. Using a computational model that combines elements of both existing theories, we show that interactions between an ictal wavefront and fixed source reproduce the traveling wave dynamics observed in vivo We conclude that combining both existing theories can generate the diversity of ictal traveling waves.SIGNIFICANCE STATEMENT The source of voltage discharges that propagate across cortex during human seizures remains unknown. Two candidate theories exist, each proposing a different discharge source. Support for each theory consists of observations from a small number of human subject recordings, analyzed with separately developed methods. How the different, limited data and different analysis methods impact the evidence for each theory is unclear. To resolve these differences, we combine the unique, human microelectrode array recordings collected separately for each theory and analyze these combined data with a unified approach. We show that neither existing theory adequately describes the data. We then propose a new theory that unifies existing proposals and successfully reproduces the voltage discharge dynamics observed in vivo.
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Affiliation(s)
- Emily D Schlafly
- Graduate Program in Neuroscience, Boston University, Boston, Massachusetts 02215
| | - François A Marshall
- Department of Mathematics and Statistics & Center for Systems Neuroscience, Boston University, Boston, Massachusetts 02215
| | - Edward M Merricks
- Department of Neurology, Columbia University, New York, New York 10032
| | - Uri T Eden
- Department of Mathematics and Statistics & Center for Systems Neuroscience, Boston University, Boston, Massachusetts 02215
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston, Massachusetts 02114
| | | | - Mark A Kramer
- Department of Mathematics and Statistics & Center for Systems Neuroscience, Boston University, Boston, Massachusetts 02215
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34
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Moosavi SA, Jirsa VK, Truccolo W. Critical dynamics in the spread of focal epileptic seizures: Network connectivity, neural excitability and phase transitions. PLoS One 2022; 17:e0272902. [PMID: 35998146 PMCID: PMC9397939 DOI: 10.1371/journal.pone.0272902] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 07/29/2022] [Indexed: 11/24/2022] Open
Abstract
Focal epileptic seizures can remain localized or, alternatively, spread across brain areas, often resulting in impairment of cognitive function and loss of consciousness. Understanding the factors that promote spread is important for developing better therapeutic approaches. Here, we show that: (1) seizure spread undergoes “critical” phase transitions in models (epileptor-networks) that capture the neural dynamics of spontaneous seizures while incorporating patient-specific brain network connectivity, axonal delays and identified epileptogenic zones (EZs). We define a collective variable for the spreading dynamics as the spread size, i.e. the number of areas or nodes in the network to which a seizure has spread. Global connectivity strength and excitability in the surrounding non-epileptic areas work as phase-transition control parameters for this collective variable. (2) Phase diagrams are predicted by stability analysis of the network dynamics. (3) In addition, the components of the Jacobian’s leading eigenvector, which tend to reflect the connectivity strength and path lengths from the EZ to surrounding areas, predict the temporal order of network-node recruitment into seizure. (4) However, stochastic fluctuations in spread size in a near-criticality region make predictability more challenging. Overall, our findings support the view that within-patient seizure-spread variability can be characterized by phase-transition dynamics under transient variations in network connectivity strength and excitability across brain areas. Furthermore, they point to the potential use and limitations of model-based prediction of seizure spread in closed-loop interventions for seizure control.
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Affiliation(s)
- S. Amin Moosavi
- Department of Neuroscience, Brown University, Providence, RI, United States of America
| | - Viktor K. Jirsa
- Aix Marseille University, INSERM, INS, Institut de Neurosciences de Système, Marseille, France
| | - Wilson Truccolo
- Department of Neuroscience, Brown University, Providence, RI, United States of America
- Carney Institute for Brain Science, Brown University, Providence, RI, United States of America
- * E-mail:
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35
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Wang X, Mi Y, Zhang Z, Chen Y, Hu G, Li H. Reconstructing distant interactions of multiple paths between perceptible nodes in dark networks. Phys Rev E 2022; 106:014302. [PMID: 35974494 DOI: 10.1103/physreve.106.014302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 06/09/2022] [Indexed: 06/15/2023]
Abstract
Quantitative research of interdisciplinary fields, including biological and social systems, has attracted great attention in recent years. Complex networks are popular and important tools for the investigations. Explosively increasing data are created by practical networks, from which useful information about dynamic networks can be extracted. From data to network structure, i.e., network reconstruction, is a crucial task. There are many difficulties in fulfilling network reconstruction, including data shortage (existence of hidden nodes) and time delay for signal propagation between adjacent nodes. In this paper a deep network reconstruction method is proposed, which can work in the conditions that even only two nodes (say A and B) are perceptible and all other network nodes are hidden. With a well-designed stochastic driving on node A, this method can reconstruct multiple interaction paths from A to B based on measured data. The distance, effective intensity, and transmission time delay of each path can be inferred accurately.
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Affiliation(s)
- Xinyu Wang
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Yuanyuan Mi
- Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing 400044, China and AI Research Center, Peng Cheng Laboratory, Shenzhen 518005, China
| | - Zhaoyang Zhang
- Department of Physics, School of Physical Science and Technology, Ningbo University, Ningbo, Zhejiang 315211, China
| | - Yang Chen
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Gang Hu
- Department of Physics, Beijing Normal University, Beijing 100875, China
| | - Haihong Li
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
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36
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GATSMOTE: Improving Imbalanced Node Classification on Graphs via Attention and Homophily. MATHEMATICS 2022. [DOI: 10.3390/math10111799] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
In recent decades, non-invasive neuroimaging techniques and graph theories have enabled a better understanding of the structural patterns of the human brain at a macroscopic level. As one of the most widely used non-invasive techniques, an electroencephalogram (EEG) may collect non-neuronal signals from “bad channels”. Automatically detecting these bad channels represents an imbalanced classification task; research on the topic is rather limited. Because the human brain can be naturally modeled as a complex graph network based on its structural and functional characteristics, we seek to extend previous imbalanced node classification techniques to the bad-channel detection task. We specifically propose a novel edge generator considering the prominent small-world organization of the human brain network. We leverage the attention mechanism to adaptively calculate the weighted edge connections between each node and its neighboring nodes. Moreover, we follow the homophily assumption in graph theory to add edges between similar nodes. Adding new edges between nodes sharing identical labels shortens the path length, thus facilitating low-cost information messaging.
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37
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Weiss SA, Pastore T, Orosz I, Rubinstein D, Gorniak R, Waldman Z, Fried I, Wu C, Sharan A, Slezak D, Worrell G, Engel J, Sperling MR, Staba RJ. Graph theoretical measures of fast ripples support the epileptic network hypothesis. Brain Commun 2022; 4:fcac101. [PMID: 35620169 PMCID: PMC9128387 DOI: 10.1093/braincomms/fcac101] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 02/10/2022] [Accepted: 04/12/2022] [Indexed: 11/16/2022] Open
Abstract
The epileptic network hypothesis and epileptogenic zone hypothesis are two
theories of ictogenesis. The network hypothesis posits that coordinated activity
among interconnected nodes produces seizures. The epileptogenic zone hypothesis
posits that distinct regions are necessary and sufficient for seizure
generation. High-frequency oscillations, and particularly fast ripples, are
thought to be biomarkers of the epileptogenic zone. We sought to test these
theories by comparing high-frequency oscillation rates and networks in surgical
responders and non-responders, with no appreciable change in seizure frequency
or severity, within a retrospective cohort of 48 patients implanted with
stereo-EEG electrodes. We recorded inter-ictal activity during non-rapid eye
movement sleep and semi-automatically detected and quantified high-frequency
oscillations. Each electrode contact was localized in normalized coordinates. We
found that the accuracy of seizure onset zone electrode contact classification
using high-frequency oscillation rates was not significantly different in
surgical responders and non-responders, suggesting that in non-responders the
epileptogenic zone partially encompassed the seizure onset zone(s)
(P > 0.05). We also found that in the
responders, fast ripple on oscillations exhibited a higher spectral content in
the seizure onset zone compared with the non-seizure onset zone
(P < 1 × 10−5).
By contrast, in the non-responders, fast ripple had a lower spectral content in
the seizure onset zone
(P < 1 × 10−5).
We constructed two different networks of fast ripple with a spectral content
>350 Hz. The first was a rate–distance network that
multiplied the Euclidian distance between fast ripple-generating contacts by the
average rate of fast ripple in the two contacts. The radius of the
rate–distance network, which excluded seizure onset zone nodes,
discriminated non-responders, including patients not offered resection or
responsive neurostimulation due to diffuse multifocal onsets, with an accuracy
of 0.77 [95% confidence interval (CI) 0.56–0.98]. The second fast
ripple network was constructed using the mutual information between the timing
of the events to measure functional connectivity. For most non-responders, this
network had a longer characteristic path length, lower mean local efficiency in
the non-seizure onset zone, and a higher nodal strength among non-seizure onset
zone nodes relative to seizure onset zone nodes. The graphical theoretical
measures from the rate–distance and mutual information networks of 22
non- responsive neurostimulation treated patients was used to train a support
vector machine, which when tested on 13 distinct patients classified
non-responders with an accuracy of 0.92 (95% CI 0.75–1). These
results indicate patients who do not respond to surgery or those not selected
for resection or responsive neurostimulation can be explained by the epileptic
network hypothesis that is a decentralized network consisting of widely
distributed, hyperexcitable fast ripple-generating nodes.
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Affiliation(s)
- Shennan A Weiss
- Dept. of Neurology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, USA
| | - Tomas Pastore
- Dept. of Computer Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Iren Orosz
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Daniel Rubinstein
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Richard Gorniak
- Dept. of Neuroradiology, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Zachary Waldman
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Itzhak Fried
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Chengyuan Wu
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Ashwini Sharan
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Diego Slezak
- Dept. of Computer Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Gregory Worrell
- Dept. of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA
- Dept. of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Jerome Engel
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Michael R. Sperling
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Richard J Staba
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
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38
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John TT, Ahmed OJ. Finding a Fragile Piece to End the Seizure War. Epilepsy Curr 2022; 22:178-180. [DOI: 10.1177/15357597221094937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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39
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Cao M, Vogrin SJ, Peterson ADH, Woods W, Cook MJ, Plummer C. Dynamical Network Models From EEG and MEG for Epilepsy Surgery—A Quantitative Approach. Front Neurol 2022; 13:837893. [PMID: 35422755 PMCID: PMC9001937 DOI: 10.3389/fneur.2022.837893] [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: 12/17/2021] [Accepted: 03/01/2022] [Indexed: 11/16/2022] Open
Abstract
There is an urgent need for more informative quantitative techniques that non-invasively and objectively assess strategies for epilepsy surgery. Invasive intracranial electroencephalography (iEEG) remains the clinical gold standard to investigate the nature of the epileptogenic zone (EZ) before surgical resection. However, there are major limitations of iEEG, such as the limited spatial sampling and the degree of subjectivity inherent in the analysis and clinical interpretation of iEEG data. Recent advances in network analysis and dynamical network modeling provide a novel aspect toward a more objective assessment of the EZ. The advantage of such approaches is that they are data-driven and require less or no human input. Multiple studies have demonstrated success using these approaches when applied to iEEG data in characterizing the EZ and predicting surgical outcomes. However, the limitations of iEEG recordings equally apply to these studies—limited spatial sampling and the implicit assumption that iEEG electrodes, whether strip, grid, depth or stereo EEG (sEEG) arrays, are placed in the correct location. Therefore, it is of interest to clinicians and scientists to see whether the same analysis and modeling techniques can be applied to whole-brain, non-invasive neuroimaging data (from MRI-based techniques) and neurophysiological data (from MEG and scalp EEG recordings), thus removing the limitation of spatial sampling, while safely and objectively characterizing the EZ. This review aims to summarize current state of the art non-invasive methods that inform epilepsy surgery using network analysis and dynamical network models. We also present perspectives on future directions and clinical applications of these promising approaches.
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Affiliation(s)
- Miao Cao
- Center for MRI Research, Peking University, Beijing, China
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Simon J. Vogrin
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Andre D. H. Peterson
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - William Woods
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Mark J. Cook
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
| | - Chris Plummer
- Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia
- Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia
- School of Health Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
- *Correspondence: Chris Plummer
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