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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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Collavini S, Pérez JJ, Berjano E, Fernández-Corazza M, Oddo S, Irastorza RM. Impact of surrounding tissue-type and peri-electrode gap in stereoelectroencephalography guided (SEEG) radiofrequency thermocoagulation (RF-TC): a computational study. Int J Hyperthermia 2024; 41:2364721. [PMID: 38880496 DOI: 10.1080/02656736.2024.2364721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 06/01/2024] [Indexed: 06/18/2024] Open
Abstract
PURPOSE To use computational modeling to provide a complete and logical description of the electrical and thermal behavior during stereoelectroencephalography-guided (SEEG) radiofrequency thermo-coagulation (RF-TC). METHODS A coupled electrical-thermal model was used to obtain the temperature distributions in the tissue during RF-TC. The computer model was first validated by an ex vivo model based on liver fragments and later used to study the impact of three different factors on the coagulation zone size: 1) the difference in the tissue surrounding the electrode (gray/white matter), 2) the presence of a peri-electrode gap occupied by cerebrospinal fluid (CSF), and 3) the energy setting used (power-duration). RESULTS The model built for the experimental validation was able to predict both the evolution of impedance and the short diameter of the coagulation zone (error < 0.01 mm) reasonably well but overestimated the long diameter by 2 - 3 mm. After adapting the model to clinical conditions, the simulation showed that: 1) Impedance roll-off limited the coagulation size but involved overheating (around 100 °C); 2) The type of tissue around the contacts (gray vs. white matter) had a moderate impact on the coagulation size (maximum difference 0.84 mm), and 3) the peri-electrode gap considerably altered the temperature distributions, avoided overheating, although the diameter of the coagulation zone was not very different from the no-gap case (<0.2 mm). CONCLUSIONS This study showed that computer modeling, especially subject- and scenario-specific modeling, can be used to estimate in advance the electrical and thermal performance of the RF-TC in brain tissue.
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Affiliation(s)
- Santiago Collavini
- Institute of Engineering and Agronomy, National University Arturo Jauretche, Buenos Aires, Argentina
- Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp. "El Cruce N. Kirchner", National University A. Jauretche (UNAJ), Buenos Aires, Argentina
| | - Juan J Pérez
- BioMIT, Departamento de Ingeniería Electrónica, Universitat Politècnica de València, València, Spain
| | - Enrique Berjano
- BioMIT, Departamento de Ingeniería Electrónica, Universitat Politècnica de València, València, Spain
| | - Mariano Fernández-Corazza
- Research Institute of Electronics, Control and Signal Processing (LEICI), National University of La Plata-CONICET, La Plata, Argentina
| | - Silvia Oddo
- Neurosciences and Complex Systems Unit (EnyS), CONICET, Hosp. "El Cruce N. Kirchner", National University A. Jauretche (UNAJ), Buenos Aires, Argentina
| | - Ramiro M Irastorza
- Institute of Engineering and Agronomy, National University Arturo Jauretche, Buenos Aires, Argentina
- Institute of Physics of Liquids and Biological Systems (IFLySiB CONICET La Plata), La Plata, Argentina
- Granular Materials Group, Department of Mechanical Engineering, La Plata Regional Faculty, National Technological University, La Plata, Argentina
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Gotman J. Has recording of seizures become obsolete? Rev Neurol (Paris) 2023; 179:872-876. [PMID: 36906456 DOI: 10.1016/j.neurol.2023.01.726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/04/2023] [Indexed: 03/11/2023]
Abstract
Some patients with medically intractable epilepsy are considered for surgical treatment. In some surgical candidates, the investigation includes the placement of intracerebral electrodes and long-term monitoring to find the region of seizure onset. This region is the primary determinant of the surgical resection but about one-third of patients are not offered surgery after electrode implantation and among those operated only about 55% are seizure free after five years. This paper discusses why the primary reliance on the seizure onset maybe suboptimal and may be in part responsible for the relatively low surgical success rate. It also proposes to consider some interictal markers that may have advantages over seizure onset and may be easier to obtain.
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Affiliation(s)
- J Gotman
- Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, Québec H3A 2B4, Canada.
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Jawata A, Nicolás von E, Jean-Marc L, Giovanni P, Giorgio A, Zhengchen C, Tanguy H, Chifaou A, Hassan K, Birgit F, Jean G, Christophe G. Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas. Neuroimage 2023; 274:120158. [PMID: 37149236 DOI: 10.1016/j.neuroimage.2023.120158] [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: 10/03/2022] [Revised: 03/27/2023] [Accepted: 05/04/2023] [Indexed: 05/08/2023] Open
Abstract
BACKGROUND Magnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. METHOD We validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (iEEG) atlas (https://mni-open-ieegatlas. RESEARCH mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space by applying a forward model to the MEG-reconstructed source maps, and estimated virtual iEEG (ViEEG) potentials on each iEEG channel location; we finally quantitatively compared those with actual iEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. RESULTS The MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the ViEEG than in the iEEG were more accurately recovered. In the deep regions, MEG-estimated amplitudes were largely underestimated and the spectra were poorly recovered. Overall, our wMEM results were similar to those obtained with minimum norm or beamformer source localization. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of iEEG but detected by MEG. Importantly, we found that MEG-estimated spectra were more comparable to spectra from the iEEG atlas after the aperiodic components were removed. CONCLUSION This study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies.
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Affiliation(s)
- Afnan Jawata
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, H3A 2B4, Canada; Integrated Program in Neuroscience, McGill University, Montréal, Québec H3A 1A1, Canada; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada.
| | - Ellenrieder Nicolás von
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada
| | - Lina Jean-Marc
- Centre De Recherches En Mathématiques, Montréal, Québec H3C 3J7, Canada; Electrical Engineering Department, École De Technologie Supérieure, Montréal, Québec H3C 1K3, Canada
| | - Pellegrino Giovanni
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Arcara Giorgio
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Cai Zhengchen
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada
| | - Hedrich Tanguy
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, H3A 2B4, Canada
| | - Abdallah Chifaou
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, H3A 2B4, Canada
| | - Khajehpour Hassan
- Physics Department and PERFORM Centre, Concordia University, Montréal, Québec H4B 1R6, Canada
| | - Frauscher Birgit
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada
| | - Gotman Jean
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada
| | - Grova Christophe
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, H3A 2B4, Canada; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec H3A 2B4, Canada; Centre De Recherches En Mathématiques, Montréal, Québec H3C 3J7, Canada; Physics Department and PERFORM Centre, Concordia University, Montréal, Québec H4B 1R6, Canada.
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von Ellenrieder N, Peter-Derex L, Gotman J, Frauscher B. SleepSEEG: Automatic sleep scoring using intracranial EEG recordings only. J Neural Eng 2022; 19. [PMID: 35439736 DOI: 10.1088/1741-2552/ac6829] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 04/18/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To perform automatic sleep scoring based only on intracranial EEG, without the need for scalp electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), in order to study sleep, epilepsy, and their interaction. APPROACH Data from 33 adult patients was used for development and training of the automatic scoring algorithm using both oscillatory and non-oscillatory spectral features. The first step consisted in unsupervised clustering of channels based on feature variability. For each cluster the classification was done in two steps, a multiclass tree followed by binary classification trees to distinguish the more challenging stage N1. The test data consisted in 11 patients, in whom the classification was done independently for each channel and then combined to get a single stage per epoch. MAIN RESULTS An overall agreement of 78% was observed in the test set between the sleep scoring of the algorithm and two human experts scoring based on scalp EEG, EOG and EMG. Balanced sensitivity and specificity were obtained for the different sleep stages. The performance was excellent for stages W, N2, and N3, and good for stage R, but with high variability across patients. The performance for the challenging stage N1 was poor, but at a similar level as for published algorithms based on scalp EEG. High confidence epochs in different stages (other than N1) can be identified with median per patient specificity >80%. SIGNIFICANCE The automatic algorithm can perform sleep scoring of long term recordings of patients with intracranial electrodes undergoing presurgical evaluation in the absence of scalp EEG, EOG and EMG, which are normally required to define sleep stages but are difficult to use in the context of intracerebral studies. It also constitutes a valuable tool to generate hypotheses regarding local aspects of sleep, and will be significant for sleep evaluation in clinical epileptology and neuroscience research.
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Affiliation(s)
- Nicolás von Ellenrieder
- Montreal Neurological Institute and Hospital, McGill University, 3801 University streeet, Montreal, Quebec, H3A 2B4, CANADA
| | - Laure Peter-Derex
- PAM Team, Centre de Recherche en Neurosciences de Lyon, 95 Boulevard Pinel, Lyon, Rhône-Alpes , 69675 BRON, FRANCE
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, 3801 University St, Montreal, Quebec, H3A 2B4, CANADA
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, CANADA
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McCarty MJ, Woolnough O, Mosher JC, Seymour J, Tandon N. The Listening Zone of Human Electrocorticographic Field Potential Recordings. eNeuro 2022; 9:ENEURO.0492-21.2022. [PMID: 35410871 PMCID: PMC9034754 DOI: 10.1523/eneuro.0492-21.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 02/09/2022] [Accepted: 03/04/2022] [Indexed: 01/05/2023] Open
Abstract
Intracranial electroencephalographic (icEEG) recordings provide invaluable insights into neural dynamics in humans because of their unmatched spatiotemporal resolution. Yet, such recordings reflect the combined activity of multiple underlying generators, confounding the ability to resolve spatially distinct neural sources. To empirically quantify the listening zone of icEEG recordings, we computed correlations between signals as a function of distance (full width at half maximum; FWHM) between 8752 recording sites in 71 patients (33 female) implanted with either subdural electrodes (SDEs), stereo-encephalography electrodes (sEEG), or high-density sEEG electrodes. As expected, for both SDEs and sEEGs, higher frequency signals exhibited a sharper fall off relative to lower frequency signals. For broadband high γ (BHG) activity, the mean FWHM of SDEs (6.6 ± 2.5 mm) and sEEGs in gray matter (7.14 ± 1.7 mm) was not significantly different; however, FWHM for low frequencies recorded by sEEGs was 2.45 mm smaller than SDEs. White matter sEEGs showed much lower power for frequencies 17-200 Hz (q < 0.01) and a much broader decay (11.3 ± 3.2 mm) than gray matter electrodes (7.14 ± 1.7 mm). The use of a bipolar referencing scheme significantly lowered FWHM for sEEGs, relative to a white matter reference or a common average reference (CAR). These results outline the influence of array design, spectral bands, and referencing schema on local field potential recordings and source localization in icEEG recordings in humans. The metrics we derive have immediate relevance to the analysis and interpretation of both cognitive and epileptic data.
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Affiliation(s)
- Meredith J McCarty
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Oscar Woolnough
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - John C Mosher
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - John Seymour
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Nitin Tandon
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
- Memorial Hermann Hospital, Texas Medical Center, Houston, TX 77030
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