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Unnwongse K, Rampp S, Wehner T, Kowoll A, Parpaley Y, von Lehe M, Lanfer B, Rusiniak M, Wolters C, Wellmer J. Validating EEG source imaging using intracranial electrical stimulation. Brain Commun 2023; 5:fcad023. [PMID: 36824389 PMCID: PMC9942548 DOI: 10.1093/braincomms/fcad023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 08/31/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023] Open
Abstract
Electrical source imaging is used in presurgical epilepsy evaluation and in cognitive neurosciences to localize neuronal sources of brain potentials recorded on EEG. This study evaluates the spatial accuracy of electrical source imaging for known sources, using electrical stimulation potentials recorded on simultaneous stereo-EEG and 37-electrode scalp EEG, and identifies factors determining the localization error. In 11 patients undergoing simultaneous stereo-EEG and 37-electrode scalp EEG recordings, sequential series of 99-110 biphasic pulses (2 ms pulse width) were applied by bipolar electrical stimulation on adjacent contacts of implanted stereo-EEG electrodes. The scalp EEG correlates of stimulation potentials were recorded with a sampling rate of 30 kHz. Electrical source imaging of averaged stimulation potentials was calculated utilizing a dipole source model of peak stimulation potentials based on individual four-compartment finite element method head models with various skull conductivities (range from 0.0413 to 0.001 S/m). Fitted dipoles with a goodness of fit of ≥80% were included in the analysis. The localization error was calculated using the Euclidean distance between the estimated dipoles and the centre point of adjacent stimulating contacts. A total of 3619 stimulation locations, respectively, dipole localizations, were included in the evaluation. Mean localization errors ranged from 10.3 to 26 mm, depending on source depth and selected skull conductivity. The mean localization error increased with an increase in source depth (r(3617) = [0.19], P = 0.000) and decreased with an increase in skull conductivity (r(3617) = [-0.26], P = 0.000). High skull conductivities (0.0413-0.0118 S/m) yielded significantly lower localization errors for all source depths. For superficial sources (<20 mm from the inner skull), all skull conductivities yielded insignificantly different localization errors. However, for deeper sources, in particular >40 mm, high skull conductivities of 0.0413 and 0.0206 S/m yielded significantly lower localization errors. In relation to stimulation locations, the majority of estimated dipoles moved outward-forward-downward to inward-forward-downward with a decrease in source depth and an increase in skull conductivity. Multivariate analysis revealed that an increase in source depth, number of skull holes and white matter volume, while a decrease in skull conductivity independently led to higher localization error. This evaluation of electrical source imaging accuracy using artificial patterns with a high signal-to-noise ratio supports its application in presurgical epilepsy evaluation and cognitive neurosciences. In our artificial potential model, optimizing the selected skull conductivity minimized the localization error. Future studies should examine if this accounts for true neural signals.
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Affiliation(s)
- Kanjana Unnwongse
- Correspondence to: Kanjana Unnwongse, MD Ruhr Epileptologie, Neurologische Klinik Universitätsklinikum Knappschaftskrankenhaus Bochum In der Schornau 23-25, 44892 Bochum, Germany E-mail:
| | | | - Tim Wehner
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
| | - Annika Kowoll
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
| | - Yaroslav Parpaley
- Department of Neurosurgery, University Hospital Knappschaftskrankenhaus, Ruhr-University, 44892 Bochum, Germany
| | - Marec von Lehe
- Department of Neurosurgery, University Hospital Knappschaftskrankenhaus, Ruhr-University, 44892 Bochum, Germany
| | - Benjamin Lanfer
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
| | | | - Carsten Wolters
- Institute for Biomagnetism und Biosignalanalysis, University of Münster, 48149 Münster, Germany,Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
| | - Jörg Wellmer
- Ruhr-Epileptology, Department of Neurology, University Hospital Knappschaftskrankenhaus, Ruhr-University Bochum, 44892 Bochum, Germany
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Abdallah C, Hedrich T, Koupparis A, Afnan J, Hall JA, Gotman J, Dubeau F, von Ellenrieder N, Frauscher B, Kobayashi E, Grova C. Clinical Yield of Electromagnetic Source Imaging and Hemodynamic Responses in Epilepsy: Validation With Intracerebral Data. Neurology 2022; 98:e2499-e2511. [PMID: 35473762 PMCID: PMC9231837 DOI: 10.1212/wnl.0000000000200337] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 02/21/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Accurate delineation of the seizure-onset zone (SOZ) in focal drug-resistant epilepsy often requires stereo-EEG (SEEG) recordings. Our aims were to propose a truly objective and quantitative comparison between EEG/magnetoencephalography (MEG) source imaging (EMSI), EEG/fMRI responses for similar spikes with primary irritative zone (PIZ) and SOZ defined by SEEG and to evaluate the value of EMSI and EEG/fMRI to predict postsurgical outcome. METHODS We identified patients with drug-resistant epilepsy who underwent EEG/MEG, EEG/fMRI, and subsequent SEEG at the Epilepsy Service from the Montreal Neurological Institute and Hospital. We quantified multimodal concordance within the SEEG channel space as spatial overlap with PIZ/SOZ and distances to the spike-onset, spike maximum amplitude and seizure core intracerebral channels by applying a new methodology consisting of converting EMSI results into SEEG electrical potentials (EMSIe-SEEG) and projecting the most significant fMRI response on the SEEG channels (fMRIp-SEEG). Spatial overlaps with PIZ/SOZ (AUCPIZ, AUCSOZ) were assessed by using the area under the receiver operating characteristic curve (AUC). Here, AUC represents the probability that a randomly picked active contact exhibited higher amplitude when located inside the spatial reference than outside. RESULTS Seventeen patients were included. Mean spatial overlaps with the PIZ and SOZ were 0.71 and 0.65 for EMSIe-SEEG and 0.57 and 0.62 for fMRIp-SEEG. Good EMSIe-SEEG spatial overlap with the PIZ was associated with smaller distance from the maximum EMSIe-SEEG contact to the spike maximum amplitude channel (median distance 14 mm). Conversely, good fMRIp-SEEG spatial overlap with the SOZ was associated with smaller distances from the maximum fMRIp-SEEG contact to the spike-onset and seizure core channels (median distances 10 and 5 mm, respectively). Surgical outcomes were correctly predicted by EEG/MEG in 12 of 15 (80%) patients and EEG/fMRI in 6 of 11(54%) patients. DISCUSSION With the use of a unique quantitative approach estimating EMSI and fMRI results in the reference SEEG channel space, EEG/MEG and EEG/fMRI accurately localized the SOZ and the PIZ. Precisely, EEG/MEG more accurately localized the PIZ, whereas EEG/fMRI was more sensitive to the SOZ. Both neuroimaging techniques provide complementary localization that can help guide SEEG implantation and select good candidates for surgery.
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Affiliation(s)
- Chifaou Abdallah
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada.
| | - Tanguy Hedrich
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Andreas Koupparis
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Jawata Afnan
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Jeffrey Alan Hall
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Jean Gotman
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Francois Dubeau
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Nicolas von Ellenrieder
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Eliane Kobayashi
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
| | - Christophe Grova
- From the Multimodal Functional Imaging Lab (C.A., T.H., J.A., C.G.), Biomedical Engineering Department, Montreal Neurological Institute and Hospital (C.A., A.K., J.A., J.A.H., J.G., F.D., N.v.E., B.F., E.K., C.G.), Neurology and Neurosurgery Department, and Analytical Neurophysiology Lab (T.H., B.F.), McGill University; and Multimodal Functional Imaging Lab (C.G.), PERFORM Centre, Department of Physics, Concordia University, Montreal, Quebec, Canada
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Zhang W, Yu T, Liao Y, Liu S, Xu M, Yang C, Lui S, Ning G, Qu H. Distinct changes of brain cortical thickness relate to post-treatment outcomes in children with epilepsy. Seizure 2021; 91:181-188. [PMID: 34174692 DOI: 10.1016/j.seizure.2021.06.010] [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: 02/23/2021] [Revised: 06/07/2021] [Accepted: 06/09/2021] [Indexed: 02/05/2023] Open
Abstract
PURPOSE In the current study, we examined the potential of neuroanatomic measures to cluster patients into different subgroups and established their clinical relevance to post-treatment outcomes. METHODS We included seventy-two children with epilepsy (aged 14-195 months) who were treated with anti-seizure medication alone and 39 healthy participants (aged 36-60 months). High-resolution T1-weighted imaging was performed for all participants, and brain cortical thickness measurements were obtained for 68 cortical regions for each of them. Amongst the patients, data-driven hierarchical cluster analysis was performed using the selected cortical thickness measures as features. The average thickness measures in each of the 68 brain regions were then compared between patient subgroups and healthy controls. RESULTS Two distinct patient subgroups were identified but were not related to the clinical types. Patients within subgroup 1 (n = 56) had a significantly higher rate of recurrent seizure than those in subgroup 2 (n = 16) (41.1% vs. 14.3%, p<0.05), while the follow-up time or medication did not differ between them. This finding was further confirmed by a recent follow-up through phone calls. The demographic variables, rate of electroencephalogram abnormalities, or sleep problems did not significantly differ between patient subgroups. Compared with healthy controls, patients in subgroup 1 showed significantly increased cortical thickness in the neocortex, whereas patients in subgroup 2 only showed regional cortical thinning in the right superior temporal gyrus. CONCLUSION These findings suggest the potential existence of distinct subgroups of children with epilepsy that were especially relevant to the differential patterns of post-treatment outcomes, with regional cortical thinning in the temporal regions relative to controls predicting lower risk of recurrent seizure.
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Affiliation(s)
- Wenjing Zhang
- Department of Radiology, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, National Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu 610041, China; Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Tao Yu
- Department of Paediatrics, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, National Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Yi Liao
- Department of Radiology, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, National Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Sai Liu
- Department of Radiology, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, National Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Mengyuan Xu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Chengmin Yang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Gang Ning
- Department of Radiology, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, National Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Haibo Qu
- Department of Radiology, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, National Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu 610041, China.
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