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Vorwerk J, Wolters CH, Baumgarten D. Global sensitivity of EEG source analysis to tissue conductivity uncertainties. Front Hum Neurosci 2024; 18:1335212. [PMID: 38532791 PMCID: PMC10963400 DOI: 10.3389/fnhum.2024.1335212] [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: 11/08/2023] [Accepted: 01/22/2024] [Indexed: 03/28/2024] Open
Abstract
Introduction To reliably solve the EEG inverse problem, accurate EEG forward solutions based on a detailed, individual volume conductor model of the head are essential. A crucial-but often neglected-aspect in generating a volume conductor model is the choice of the tissue conductivities, as these may vary from subject to subject. In this study, we investigate the sensitivity of EEG forward and inverse solutions to tissue conductivity uncertainties for sources distributed over the whole cortex surface. Methods We employ a detailed five-compartment head model distinguishing skin, skull, cerebrospinal fluid, gray matter, and white matter, where we consider uncertainties of skin, skull, gray matter, and white matter conductivities. We use the finite element method (FEM) to calculate EEG forward solutions and goal function scans (GFS) as inverse approach. To be able to generate the large number of EEG forward solutions, we employ generalized polynomial chaos (gPC) expansions. Results For sources up to a depth of 4 cm, we find the strongest influence on the signal topography of EEG forward solutions for the skull conductivity and a notable effect for the skin conductivity. For even deeper sources, e.g., located deep in the longitudinal fissure, we find an increasing influence of the white matter conductivity. The conductivity variations translate to varying source localizations particularly for quasi-tangential sources on sulcal walls, whereas source localizations of quasi-radial sources on the top of gyri are less affected. We find a strong correlation between skull conductivity and the variation of source localizations and especially the depth of the reconstructed source for quasi-tangential sources. We furthermore find a clear but weaker correlation between depth of the reconstructed source and the skin conductivity. Discussion Our results clearly show the influence of tissue conductivity uncertainties on EEG source analysis. We find a particularly strong influence of skull and skin conductivity uncertainties.
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Affiliation(s)
- Johannes Vorwerk
- Institute of Electrical and Biomedical Engineering, UMIT TIROL—Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
| | - Carsten H. Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Daniel Baumgarten
- Institute of Electrical and Biomedical Engineering, UMIT TIROL—Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
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2
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Knowlton RC. Ictal EEG Source Imaging. J Clin Neurophysiol 2024; 41:27-35. [PMID: 38181385 DOI: 10.1097/wnp.0000000000001033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024] Open
Abstract
SUMMARY Ictal EEG source imaging (ESI) is an advancing and growing application for presurgical epilepsy evaluation. For far too long, localization of seizures with scalp EEG has continued to rely on visual inspection of tracings arranged in a variety of montages allowing, at best, rough estimates of seizure onset regions. This most critical step is arguably the weakest point in epilepsy localization for surgical decision-making in clinical practice today. This review covers the methods and strategies that have been developed and tested for the performance of ictal ESI. It highlights practical issues and solutions toward sound implementation while covering differing methods to tackle the challenges specific to ictal ESI-noise and artifact reduction, component analysis, and other tools to increase seizure-specific signal for analysis. Further, validation studies to date-those with both high and low density numbers of electrodes-are summarized, providing a glimpse at the relative accuracy of ictal ESI in all types of focal epilepsy patients. Finally, given the added noninvasive information (greater degree of spatial resolution compared with standard ictal EEG review), the role of ictal ESI and its clinical utility in the presurgical evaluation is discussed.
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Affiliation(s)
- Robert C Knowlton
- Departments of Neurology, Radiology, and Neurological Surgery, University of California San Francisco, San Francisco, California, U.S.A
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3
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Horrillo-Maysonnial A, Avigdor T, Abdallah C, Mansilla D, Thomas J, von Ellenrieder N, Royer J, Bernhardt B, Grova C, Gotman J, Frauscher B. Targeted density electrode placement achieves high concordance with traditional high-density EEG for electrical source imaging in epilepsy. Clin Neurophysiol 2023; 156:262-271. [PMID: 37704552 DOI: 10.1016/j.clinph.2023.08.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/27/2023] [Accepted: 08/12/2023] [Indexed: 09/15/2023]
Abstract
OBJECTIVE High-density (HD) electroencephalography (EEG) is increasingly used in presurgical epilepsy evaluation, but it is demanding in time and resources. To overcome these issues, we compared EEG source imaging (ESI) solutions with a targeted density and HD-EEG montage. METHODS HD-EEGs from patients undergoing presurgical evaluation were analyzed. A low-density recording was created by selecting the 25 electrodes of a standard montage from the 83 electrodes of the HD-EEG and adding 8-11 electrodes around the electrode with the highest amplitude interictal epileptiform discharges. The ESI solution from this "targeted" montage was compared to that from the HD-EEG using the distance between peak vertices, sublobar concordance and a qualitative similarity measure. RESULTS Fifty-eight foci of forty-three patients were included. The median distance between the peak vertices of the two montages was 13.2 mm, irrespective of focus' location. Tangential generators (n = 5/58) showed a higher distance than radial generators (p = 0.04). We found sublobar concordance in 54/58 of the foci (93%). Map similarity, assessed by an epileptologist, had a median score of 4/5. CONCLUSIONS ESI solutions obtained from a targeted density montage show high concordance with those calculated from HD-EEG. SIGNIFICANCE Requiring significantly fewer electrodes, targeted density EEG allows obtaining similar ESI solutions as traditional HD-EEG montage.
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Affiliation(s)
- A Horrillo-Maysonnial
- Clinical Neurophysiology Section, Clínica Universidad de Navarra, Pamplona, Spain; IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain; Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - T Avigdor
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Canada.
| | - C Abdallah
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Canada.
| | - D Mansilla
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - J Thomas
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - N von Ellenrieder
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - J Royer
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - B Bernhardt
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - C Grova
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Canada; Multimodal Functional Imaging Lab, PERFORM Center, Department of Physics, Concordia University, Montreal, QC, Canada.
| | - J Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - B Frauscher
- Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Department of Neurology, Duke University Medical Center, Durham, NC, United States; Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, United States.
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Mohammed AH, Cabrerizo M, Pinzon A, Yaylali I, Jayakar P, Adjouadi M. Graph neural networks in EEG spike detection. Artif Intell Med 2023; 145:102663. [PMID: 37925203 DOI: 10.1016/j.artmed.2023.102663] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 06/06/2023] [Accepted: 09/14/2023] [Indexed: 11/06/2023]
Abstract
OBJECTIVE This study develops new machine learning architectures that are more adept at detecting interictal epileptiform discharges (IEDs) in scalp EEG. A comparison of results using the average precision (AP) metric is made with the proposed models on two datasets obtained from Baptist Hospital of Miami and Temple University Hospital. METHODS Applying graph neural networks (GNNs) on functional connectivity (FC) maps of different frequency sub-bands to yield a novel architecture we call FC-GNN. Attention mechanism is applied on a complete graph to let the neural network select its important edges, hence bypassing the extraction of features, a model we refer to as CA-GNN. RESULTS On the Baptist Hospital dataset, the results were as follows: Vanilla Self-Attention →0.9029±0.0431, Hierarchical Attention →0.8546±0.0587, Vanilla Visual Geometry Group (VGG) →0.92±0.0618, Satelight →0.9219±0.046, FC-GNN →0.9731±0.0187, and CA-GNN →0.9788±0.0125. In the same order, the results on the Temple University Hospital dataset are 0.9692, 0.9113, 0.97, 0.9575, 0.963, and 0.9879. CONCLUSION Based on the good results they yield, GNNs prove to have a strong potential in detecting epileptogenic activity. SIGNIFICANCE This study opens the door for the discovery of the powerful role played by GNNs in capturing IEDs, which is an essential step for identifying the epileptogenic networks of the affected brain and hence improving the prospects for more accurate 3D source localization.
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Affiliation(s)
- Ahmed Hossam Mohammed
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA.
| | - Mercedes Cabrerizo
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA
| | - Alberto Pinzon
- Epilepsy Center, Baptist Hospital of Miami, 9090 SW 87th Ct Suite201, Miami, 33176, FL, USA
| | - Ilker Yaylali
- Department of Neurology, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, 97239, OR, USA
| | - Prasanna Jayakar
- Brain Institute, Nicklaus Children's Hospital, 3100 SW 62nd Ave, Miami, FL 33155, USA
| | - Malek Adjouadi
- Department of Electrical and Computer Engineering, Florida International University, 10555 W Flagler St, Miami, 33174, FL, USA
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Loube DK, Tan YL, Yoshii-Contreras J, Kleen J, Rao VR, Chang EF, Knowlton RC. Ictal EEG Source Imaging With Supplemental Electrodes. J Clin Neurophysiol 2023:00004691-990000000-00102. [PMID: 37820169 DOI: 10.1097/wnp.0000000000001025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023] Open
Abstract
INTRODUCTION Noninvasive brain imaging tests play a major role in guiding decision-making and the usage of invasive, costly intracranial electroencephalogram (ICEEG) in the presurgical epilepsy evaluation. This study prospectively examined the concordance in localization between ictal EEG source imaging (ESI) and ICEEG as a reference standard. METHODS Between August 2014 and April 2019, patients during video monitoring with scalp EEG were screened for those with intractable focal epilepsy believed to be amenable to surgical treatment. Additional 10-10 electrodes (total = 31-38 per patient, "31+") were placed over suspected regions of seizure onset in 104 patients. Of 42 patients requiring ICEEG, 30 (mean age 30, range 19-59) had sufficiently localized subsequent intracranial studies to allow comparison of localization between tests. ESI was performed using realistic forward boundary element models used in dipole and distributed source analyses. RESULTS At least partial sublobar concordance between ESI and ICEEG solutions was obtained in 97% of cases, with 73% achieving complete agreement. Median Euclidean distances between ESI and ICEEG solutions ranged from 25 to 30 mm (dipole) and 23 to 38 mm (distributed source). The latter was significantly more accurate with 31+ compared with 21 electrodes (P < 0.01). A difference of ≤25 mm was present in two thirds of the cases. No significant difference was found between dipole and distributed source analyses. CONCLUSIONS A practical method of ictal ESI (nonuniform placement of 31-38 electrodes) yields high accuracy for seizure localization in epilepsy surgery candidates. These results support routine clinical application of ESI in the presurgical evaluation.
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Affiliation(s)
| | - Yee-Leng Tan
- Department of Neurology, National Neuroscience Institute, SingHealth, Republic of Singapore
| | - June Yoshii-Contreras
- Division of Epilepsy, Department of Neurology, University of California San Diego, California, U.S.A; and
| | - Jonathan Kleen
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, U.S.A
| | - Vikram R Rao
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, U.S.A
| | - Edward F Chang
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, U.S.A
| | - Robert C Knowlton
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, U.S.A
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Hatlestad-Hall C, Bruña R, Liljeström M, Renvall H, Heuser K, Taubøll E, Maestú F, Haraldsen IH. Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough? Clin Neurophysiol 2023; 150:1-16. [PMID: 36972647 DOI: 10.1016/j.clinph.2023.03.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/03/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023]
Abstract
OBJECTIVE Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities. METHODS EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were subsequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested. RESULTS The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the network metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated. CONCLUSIONS Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 electrodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data. SIGNIFICANCE Characterisation of functional brain networks derived from low-density EEG warrants careful consideration.
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Affiliation(s)
| | - Ricardo Bruña
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Radiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Mia Liljeström
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki, Finland
| | - Kjell Heuser
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway; BrainSymph AS, Oslo, Norway
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Poncet M, Ales JM. Estimating neural activity from visual areas using functionally defined EEG templates. Hum Brain Mapp 2023; 44:1846-1861. [PMID: 36655286 PMCID: PMC9980892 DOI: 10.1002/hbm.26188] [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: 08/08/2022] [Revised: 12/01/2022] [Accepted: 12/11/2022] [Indexed: 01/20/2023] Open
Abstract
Electroencephalography (EEG) is a common and inexpensive method to record neural activity in humans. However, it lacks spatial resolution making it difficult to determine which areas of the brain are responsible for the observed EEG response. Here we present a new easy-to-use method that relies on EEG topographical templates. Using MRI and fMRI scans of 50 participants, we simulated how the activity in each visual area appears on the scalp and averaged this signal to produce functionally defined EEG templates. Once created, these templates can be used to estimate how much each visual area contributes to the observed EEG activity. We tested this method on extensive simulations and on real data. The proposed procedure is as good as bespoke individual source localization methods, robust to a wide range of factors, and has several strengths. First, because it does not rely on individual brain scans, it is inexpensive and can be used on any EEG data set, past or present. Second, the results are readily interpretable in terms of functional brain regions and can be compared across neuroimaging techniques. Finally, this method is easy to understand, simple to use and expandable to other brain sources.
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Affiliation(s)
- Marlene Poncet
- School of Psychology and NeuroscienceUniversity of St AndrewsSt AndrewsUK
| | - Justin M. Ales
- School of Psychology and NeuroscienceUniversity of St AndrewsSt AndrewsUK
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Wang Z, Mengoni P. Seizure classification with selected frequency bands and EEG montages: a Natural Language Processing approach. Brain Inform 2022; 9:11. [PMID: 35622175 PMCID: PMC9142724 DOI: 10.1186/s40708-022-00159-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/17/2022] [Indexed: 11/10/2022] Open
Abstract
Individualized treatment is crucial for epileptic patients with different types of seizures. The differences among patients impact the drug choice as well as the surgery procedure. With the advance in machine learning, automatic seizure detection can ease the manual time-consuming and labor-intensive procedure for diagnose seizure in the clinical setting. In this paper, we present an electroencephalography (EEG) frequency bands (sub-bands) and montages selection (sub-zones) method for classifier training that exploits Natural Language Processing from individual patients' clinical report. The proposed approach is targeting for individualized treatment. We integrated the prior knowledge from patient's reports into the classifier-building process, mimicking the authentic thinking process of experienced neurologist's when diagnosing seizure using EEG. The keywords from clinical documents are mapped to the EEG data in terms of frequency bands and scalp EEG electrodes. The data of experiments are from the Temple University Hospital EEG seizure corpus, and the dataset is divided based on each group of patients with same seizure type and same recording electrode references. The classifier includes Random Forest, Support Vector Machine and Multi-Layer Perceptron. The classification performance indicates that competitive results can be achieve with a small portion of EEG the data. Using the sub-zones selection for Generalized Seizures (GNSZ) on all three electrodes, data are reduced by nearly 50% while the performance metrics remain at the same level with the whole frequency and zones. Moreover, when selecting by sub-zones and sub-bands together for GNSZ with Linked Ears reference, the data range reduced to 0.3% of whole range, and the performance deviates less than 3% from the results with whole range of data. Results show that using proposed approach may lead to more efficient implementations of the seizure classifier to be executed on power-efficient devices for long lasting real-time seizures detection.
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Affiliation(s)
- Ziwei Wang
- Institute of Interdisciplinary Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China
| | - Paolo Mengoni
- Department of Journalism, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China.
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Emotion discrimination using source connectivity analysis based on dynamic ROI identification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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10
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Thurairajah A, Freibauer A, RamachandranNair R, Whitney R, Jain P, Donner E, Widjaja E, Jones KC. Low density electrical source imaging of the ictal onset zone in the surgical evaluation of children with epilepsy. Epilepsy Res 2021; 178:106810. [PMID: 34784573 DOI: 10.1016/j.eplepsyres.2021.106810] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 10/18/2021] [Accepted: 11/02/2021] [Indexed: 01/14/2023]
Abstract
PURPOSE To investigate the utility of Low Density (LD) Electrical Source Imaging (ESI) to model the ictal onset zone (IOZ) for the surgical work up of children with medically refractory epilepsy. METHODS This was a retrospective review of 12 patients from a district and regional pediatric epilepsy center, who underwent focal resections between 2014 and 2019. ESI was generated using the Curry 8 software, incorporating T1 Magnetic Resonance Imaging (MRI) scans and scalp electroencephalogram (EEG) recordings. Concordance of the ictal LD-ESI localizations to the epileptogenic zone was assessed by comparing the location of the ictal LD-ESI to the focal resection margins on neuroimaging and noting the post-operative outcomes at one year. Localizations determined by ictal LD-ESI were also compared to interictal LD-ESI, positron emission tomography (FDG-PET) and interictal magnetoencephalography (MEG). RESULTS Ictal ESI correctly localized the ictal onset zone in 4/6 patients, with all four being seizure free at one year. Similarly, interictal ESI localized the irritative zone in 7/9 patients with focal resections, with 6/7 being seizure free at one year. Additionally, we observed ictal ESI to be concordant to interictal ESI in 5/6 patients. Ictal ESI and interictal ESI were concordant to interictal MEG in 3/6 patients. Ictal ESI was concordant with FDG-PET in 6/7 cases. CONCLUSION IOZ source localization through LD-ESI is a promising complementary method of assessing the epileptogenic focus in children. These findings may support the inclusion of ictal LD-ESI within the pre-surgical evaluation of children to supplement current diagnostic tools.
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Affiliation(s)
- Arun Thurairajah
- The Division of Neurology, Department of Pediatrics, McMaster Children's Hospital, Hamilton, ON, Canada
| | - Alexander Freibauer
- The Division of Neurology, Department of Pediatrics, McMaster Children's Hospital, Hamilton, ON, Canada
| | - Rajesh RamachandranNair
- The Division of Neurology, Department of Pediatrics, McMaster Children's Hospital, Hamilton, ON, Canada
| | - Robyn Whitney
- The Division of Neurology, Department of Pediatrics, McMaster Children's Hospital, Hamilton, ON, Canada
| | - Puneet Jain
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Elizabeth Donner
- Division of Neurology, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON, Canada
| | - Elysa Widjaja
- The Division of Neuroimaging, Department of Diagnostic Imaging, The Hospital for Sick Children Toronto ON, Canada
| | - Kevin C Jones
- The Division of Neurology, Department of Pediatrics, McMaster Children's Hospital, Hamilton, ON, Canada.
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Quintiliani M, Bianchi F, Fuggetta F, Chieffo DPR, Ramaglia A, Battaglia DI, Tamburrini G. Role of high-density EEG (hdEEG) in pre-surgical epilepsy evaluation in children: case report and review of the literature. Childs Nerv Syst 2021; 37:1429-1437. [PMID: 33604716 PMCID: PMC8084826 DOI: 10.1007/s00381-021-05069-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 02/02/2021] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Electrical source imaging (ESI) and especially hdEEG represent a noninvasive, low cost and accurate method of localizing epileptic zone (EZ). Such capability can greatly increase seizure freedom rate in surgically treated drug resistant epilepsy cases. Furthermore, ESI might be important in intracranial record planning. CASE REPORT We report the case of a 15 years old boy suffering from drug resistant epilepsy with a previous history of DNET removal. The patient suffered from heterogeneous seizure semiology characterized by anesthesia and loss of tone in the left arm, twisting of the jaw to the left and dysarthria accompanied by daze; lightheadedness sometimes associated with headache and dizziness and at a relatively short time distance negative myoclonus involving the left hand. Clinical evidence poorly match scalp and video EEG monitoring thus requiring hdEEG recording followed by SEEG to define surgical target. Surgery was also guided by ECoG and obtained seizure freedom. DISCUSSION ESI offers an excellent estimate of EZ, being hdEEG and intracranial recordings especially important in defining it. We analyzed our results together with the data from the literature showing how in children hdEEG might be even more crucial than in adults due to the heterogeneity in seizures phenomenology. The complexity of each case and the technical difficulties in dealing with children, stress even more the importance of a noninvasive tool for diagnosis. In fact, hdEEG not only guided in the presented case SEEG planning but may also in the future offer the possibility to replace it.
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Affiliation(s)
- Michela Quintiliani
- Infantile Neuropsychiatry, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
| | - Federico Bianchi
- Pediatric Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo F. Vito 1, 00168, Rome, Italy.
| | - Filomena Fuggetta
- Infantile Neuropsychiatry, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
| | | | - Antonia Ramaglia
- Institute of Radiology, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
| | - Domenica Immacolata Battaglia
- Infantile Neuropsychiatry, Fondazione Policlinico Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Gianpiero Tamburrini
- Pediatric Neurosurgery, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo F. Vito 1, 00168, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
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Wang D, Liu Z, Tao Y, Chen W, Chen B, Wang Q, Yan X, Wang G. Improvement in EEG Source Imaging Accuracy by Means of Wavelet Packet Transform and Subspace Component Selection. IEEE Trans Neural Syst Rehabil Eng 2021; 29:650-661. [PMID: 33687844 DOI: 10.1109/tnsre.2021.3064665] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The electroencephalograph (EEG) source imaging (ESI) method is a non-invasive method that provides high temporal resolution imaging of brain electrical activity on the cortex. However, because the accuracy of EEG source imaging is often affected by unwanted signals such as noise or other source-irrelevant signals, the results of ESI are often incongruous with the real sources of brain activities. This study presents a novel ESI method (WPESI) that is based on wavelet packet transform (WPT) and subspace component selection to image the cerebral activities of EEG signals on the cortex. First, the original EEG signals are decomposed into several subspace components by WPT. Second, the subspaces associated with brain sources are selected and the relevant signals are reconstructed by WPT. Finally, the current density distribution in the cerebral cortex is obtained by establishing a boundary element model (BEM) from head MRI and applying the appropriate inverse calculation. In this study, the localization results obtained by this proposed approach were better than those of the original sLORETA approach (OESI) in the computer simulations and visual evoked potential (VEP) experiments. For epilepsy patients, the activity sources estimated by this proposed algorithm conformed to the seizure onset zones. The WPESI approach is easy to implement achieved favorable accuracy in terms of EEG source imaging. This demonstrates the potential for use of the WPESI algorithm to localize epileptogenic foci from scalp EEG signals.
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13
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Vorderwülbecke BJ, Carboni M, Tourbier S, Brunet D, Seeber M, Spinelli L, Seeck M, Vulliemoz S. High-density Electric Source Imaging of interictal epileptic discharges: How many electrodes and which time point? Clin Neurophysiol 2020; 131:2795-2803. [PMID: 33137569 DOI: 10.1016/j.clinph.2020.09.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/31/2020] [Accepted: 09/07/2020] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To assess the value of caudal EEG electrodes over cheeks and neck for high-density electric source imaging (ESI) in presurgical epilepsy evaluation, and to identify the best time point during averaged interictal epileptic discharges (IEDs) for optimal ESI accuracy. METHODS We retrospectively examined presurgical 257-channel EEG recordings of 45 patients with pharmacoresistant focal epilepsy. By stepwise removal of cheek and neck electrodes, averaged IEDs were downsampled to 219, 204, and 156 EEG channels. Additionally, ESI at the IED's half-rise was compared to other time points. The respective sources of maximum activity were compared to the resected brain area and postsurgical outcome. RESULTS Caudal channels had disproportionately more artefacts. In 30 patients with favourable outcome, the 204-channel array yielded the most accurate results with ESI maxima < 10 mm from the resection in 67% and inside affected sublobes in 83%. Neither in temporal nor in extratemporal cases did the full 257-channel setup improve ESI accuracy. ESI was most accurate at 50% of the IED's rising phase. CONCLUSION Information from cheeks and neck electrodes did not improve high-density ESI accuracy, probably due to higher artefact load and suboptimal biophysical modelling. SIGNIFICANCE Very caudal EEG electrodes should be used for ESI with caution.
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Affiliation(s)
- Bernd J Vorderwülbecke
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland; Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
| | - Margherita Carboni
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland; Functional Brain Mapping Lab, Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, 1202 Geneva, Switzerland.
| | - Sebastien Tourbier
- Connectomics Lab, Department of Radiology, Lausanne University Hospital, Rue du Bugnon 46, 1011 Lausanne, Switzerland.
| | - Denis Brunet
- Functional Brain Mapping Lab, Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, 1202 Geneva, Switzerland.
| | - Martin Seeber
- Functional Brain Mapping Lab, Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, 1202 Geneva, Switzerland.
| | - Laurent Spinelli
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
| | - Margitta Seeck
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
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14
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Grigorovsky V, Jacobs D, Breton VL, Tufa U, Lucasius C, Del Campo JM, Chinvarun Y, Carlen PL, Wennberg R, Bardakjian BL. Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression. Brain Commun 2020; 2:fcaa182. [PMID: 33376988 PMCID: PMC7750942 DOI: 10.1093/braincomms/fcaa182] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 09/22/2020] [Accepted: 09/24/2020] [Indexed: 12/15/2022] Open
Abstract
Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21–41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5–4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5–1.5 Hz signal and amplitude of 30–50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier—a hidden Markov model—was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.
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Affiliation(s)
| | - Daniel Jacobs
- Institute of Biomedical Engineering, University of Toronto, Canada
| | | | - Uilki Tufa
- Institute of Biomedical Engineering, University of Toronto, Canada
| | - Christopher Lucasius
- Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Canada
| | | | - Yotin Chinvarun
- Comprehensive Epilepsy Program and Neurology Unit, Phramongkutklao Hospital, Thailand
| | - Peter L Carlen
- Institute of Biomedical Engineering, University of Toronto, Canada.,Department of Physiology, University of Toronto, Canada.,Division of Neurology, Toronto Western Hospital, Canada
| | | | - Berj L Bardakjian
- Institute of Biomedical Engineering, University of Toronto, Canada.,Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Canada
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15
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Ye S, Yang L, Lu Y, Kucewicz MT, Brinkmann B, Nelson C, Sohrabpour A, Worrell GA, He B. Contribution of Ictal Source Imaging for Localizing Seizure Onset Zone in Patients With Focal Epilepsy. Neurology 2020; 96:e366-e375. [PMID: 33097598 DOI: 10.1212/wnl.0000000000011109] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 09/01/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To determine whether seizure onset zone (SOZ) can be localized accurately prior to surgical planning in patients with focal epilepsy, we performed noninvasive EEG recordings and source localization analyses on 39 patients. METHODS In 39 patients with focal epilepsy, we recorded and extracted 138 seizures and 1,325 interictal epileptic discharges using high-density EEG. We investigated a novel approach for directly imaging sources of seizures and interictal spikes from high-density EEG recordings, and rigorously validated it for noninvasive localization of SOZ determined from intracranial EEG findings and surgical resection volume. Conventional source imaging analyses were also performed for comparison. RESULTS Ictal source imaging showed a concordance rate of 95% when compared to intracranial EEG or resection results. The average distance from estimation to seizure onset (intracranial) electrodes is 1.35 cm in patients with concordant results, and 0.74 cm to surgical resection boundary in patients with successful surgery. About 41% of the patients were found to have multiple types of interictal activities; coincidentally, a lower concordance rate and a significantly worse performance in localizing SOZ were observed in these patients. CONCLUSION Noninvasive ictal source imaging with high-density EEG recording can provide highly concordant results with clinical decisions obtained by invasive monitoring or confirmed by resective surgery. By means of direct seizure imaging using high-density scalp EEG recordings, the added value of ictal source imaging is particularly high in patients with complex interictal activity patterns, who may represent the most challenging cases with poor prognosis.
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Affiliation(s)
- Shuai Ye
- From the Department of Biomedical Engineering (S.Y., A.S., B.H.), Carnegie Mellon University, Pittsburgh, PA; Department of Biomedical Engineering (L.Y., Y.L.), University of Minnesota, Minneapolis; Mayo Clinic (M.T.K., B.B., C.N., G.A.W.), Rochester, MN
| | - Lin Yang
- From the Department of Biomedical Engineering (S.Y., A.S., B.H.), Carnegie Mellon University, Pittsburgh, PA; Department of Biomedical Engineering (L.Y., Y.L.), University of Minnesota, Minneapolis; Mayo Clinic (M.T.K., B.B., C.N., G.A.W.), Rochester, MN
| | - Yunfeng Lu
- From the Department of Biomedical Engineering (S.Y., A.S., B.H.), Carnegie Mellon University, Pittsburgh, PA; Department of Biomedical Engineering (L.Y., Y.L.), University of Minnesota, Minneapolis; Mayo Clinic (M.T.K., B.B., C.N., G.A.W.), Rochester, MN
| | - Michal T Kucewicz
- From the Department of Biomedical Engineering (S.Y., A.S., B.H.), Carnegie Mellon University, Pittsburgh, PA; Department of Biomedical Engineering (L.Y., Y.L.), University of Minnesota, Minneapolis; Mayo Clinic (M.T.K., B.B., C.N., G.A.W.), Rochester, MN
| | - Benjamin Brinkmann
- From the Department of Biomedical Engineering (S.Y., A.S., B.H.), Carnegie Mellon University, Pittsburgh, PA; Department of Biomedical Engineering (L.Y., Y.L.), University of Minnesota, Minneapolis; Mayo Clinic (M.T.K., B.B., C.N., G.A.W.), Rochester, MN
| | - Cindy Nelson
- From the Department of Biomedical Engineering (S.Y., A.S., B.H.), Carnegie Mellon University, Pittsburgh, PA; Department of Biomedical Engineering (L.Y., Y.L.), University of Minnesota, Minneapolis; Mayo Clinic (M.T.K., B.B., C.N., G.A.W.), Rochester, MN
| | - Abbas Sohrabpour
- From the Department of Biomedical Engineering (S.Y., A.S., B.H.), Carnegie Mellon University, Pittsburgh, PA; Department of Biomedical Engineering (L.Y., Y.L.), University of Minnesota, Minneapolis; Mayo Clinic (M.T.K., B.B., C.N., G.A.W.), Rochester, MN
| | - Gregory A Worrell
- From the Department of Biomedical Engineering (S.Y., A.S., B.H.), Carnegie Mellon University, Pittsburgh, PA; Department of Biomedical Engineering (L.Y., Y.L.), University of Minnesota, Minneapolis; Mayo Clinic (M.T.K., B.B., C.N., G.A.W.), Rochester, MN
| | - Bin He
- From the Department of Biomedical Engineering (S.Y., A.S., B.H.), Carnegie Mellon University, Pittsburgh, PA; Department of Biomedical Engineering (L.Y., Y.L.), University of Minnesota, Minneapolis; Mayo Clinic (M.T.K., B.B., C.N., G.A.W.), Rochester, MN.
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16
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van Mierlo P, Vorderwülbecke BJ, Staljanssens W, Seeck M, Vulliémoz S. Ictal EEG source localization in focal epilepsy: Review and future perspectives. Clin Neurophysiol 2020; 131:2600-2616. [PMID: 32927216 DOI: 10.1016/j.clinph.2020.08.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/12/2020] [Accepted: 08/04/2020] [Indexed: 11/25/2022]
Abstract
Electroencephalographic (EEG) source imaging localizes the generators of neural activity in the brain. During presurgical epilepsy evaluation, EEG source imaging of interictal epileptiform discharges is an established tool to estimate the irritative zone. However, the origin of interictal activity can be partly or fully discordant with the origin of seizures. Therefore, source imaging based on ictal EEG data to determine the seizure onset zone can provide precious clinical information. In this descriptive review, we address the importance of localizing the seizure onset zone based on noninvasive EEG recordings as a complementary analysis that might reduce the burden of the presurgical evaluation. We identify three major challenges (low signal-to-noise ratio of the ictal EEG data, spread of ictal activity in the brain, and validation of the developed methods) and discuss practical solutions. We provide an extensive overview of the existing clinical studies to illustrate the potential clinical utility of EEG-based localization of the seizure onset zone. Finally, we conclude with future perspectives and the needs for translating ictal EEG source imaging into clinical practice.
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Affiliation(s)
- Pieter van Mierlo
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium.
| | - Bernd J Vorderwülbecke
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland; Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
| | - Willeke Staljanssens
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Margitta Seeck
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.
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17
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Vespa S, Baroumand AG, Ferrao Santos S, Vrielynck P, de Tourtchaninoff M, Feys O, Strobbe G, Raftopoulos C, van Mierlo P, El Tahry R. Ictal EEG source imaging and connectivity to localize the seizure onset zone in extratemporal lobe epilepsy. Seizure 2020; 78:18-30. [DOI: 10.1016/j.seizure.2020.03.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/27/2020] [Accepted: 03/01/2020] [Indexed: 12/16/2022] Open
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18
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Rikir E, Maillard LG, Abdallah C, Gavaret M, Bartolomei F, Vignal JP, Colnat-Coulbois S, Koessler L. Respective Contribution of Ictal and Inter-ictal Electrical Source Imaging to Epileptogenic Zone Localization. Brain Topogr 2020; 33:384-402. [DOI: 10.1007/s10548-020-00768-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/07/2020] [Indexed: 10/24/2022]
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19
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Mégevand P, Seeck M. Electric source imaging for presurgical epilepsy evaluation: current status and future prospects. Expert Rev Med Devices 2020; 17:405-412. [DOI: 10.1080/17434440.2020.1748008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- Pierre Mégevand
- Epilepsy Unit, Neurology Division, Clinical Neuroscience Department, Geneva University Hospitals, Genève, Switzerland
- Basic Neuroscience Department, Faculty of Medicine, University of Geneva, Genève, Switzerland
| | - Margitta Seeck
- Epilepsy Unit, Neurology Division, Clinical Neuroscience Department, Geneva University Hospitals, Genève, Switzerland
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20
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Grassini S, Laumann K. Questionnaire Measures and Physiological Correlates of Presence: A Systematic Review. Front Psychol 2020; 11:349. [PMID: 32265769 PMCID: PMC7096541 DOI: 10.3389/fpsyg.2020.00349] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 02/14/2020] [Indexed: 11/17/2022] Open
Abstract
The published literature has produced several definitions for the sense of presence in a simulated environment, as well as various methods for measuring it. The variety of conceptualizations makes it difficult for researchers to interpret, compare, and evaluate the presence ratings obtained from individual studies. Presence has been measured by employing questionnaires, physiological indices, behavioral feedbacks, and interviews. A systematic literature review was conducted to provide insight into the definitions and measurements of presence in studies from 2002 to 2019, with a focus on questionnaires and physiological measures. The review showed that scholars had introduced various definitions of presence that often originate from different theoretical standpoints and that this has produced a multitude of different questionnaires that aim to measure presence. At the same time, physiological studies that investigate the physiological correlates of the sense of presence have often shown ambiguous results or have not been replicated. Most of the scholars have preferred the use of questionnaires, with Witmer and Singer's Presence Questionnaire being the most prevalent. Among the physiological measures, electroencephalography was the most frequently used. The conclusions of the present review aim to stimulate future structured efforts to standardize the use of the construct of presence, as well as inspire the replication of the findings reported in the published literature.
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Affiliation(s)
- Simone Grassini
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Karin Laumann
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
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21
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Baumgartner C, Koren JP, Britto-Arias M, Zoche L, Pirker S. Presurgical epilepsy evaluation and epilepsy surgery. F1000Res 2019; 8. [PMID: 31700611 PMCID: PMC6820825 DOI: 10.12688/f1000research.17714.1] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/14/2019] [Indexed: 12/21/2022] Open
Abstract
With a prevalence of 0.8 to 1.2%, epilepsy represents one of the most frequent chronic neurological disorders; 30 to 40% of patients suffer from drug-resistant epilepsy (that is, seizures cannot be controlled adequately with antiepileptic drugs). Epilepsy surgery represents a valuable treatment option for 10 to 50% of these patients. Epilepsy surgery aims to control seizures by resection of the epileptogenic tissue while avoiding neuropsychological and other neurological deficits by sparing essential brain areas. The most common histopathological findings in epilepsy surgery specimens are hippocampal sclerosis in adults and focal cortical dysplasia in children. Whereas presurgical evaluations and surgeries in patients with mesial temporal sclerosis and benign tumors recently decreased in most centers, non-lesional patients, patients requiring intracranial recordings, and neocortical resections increased. Recent developments in neurophysiological techniques (high-density electroencephalography [EEG], magnetoencephalography, electrical and magnetic source imaging, EEG-functional magnetic resonance imaging [EEG-fMRI], and recording of pathological high-frequency oscillations), structural magnetic resonance imaging (MRI) (ultra-high-field imaging at 7 Tesla, novel imaging acquisition protocols, and advanced image analysis [post-processing] techniques), functional imaging (positron emission tomography and single-photon emission computed tomography co-registered to MRI), and fMRI significantly improved non-invasive presurgical evaluation and have opened the option of epilepsy surgery to patients previously not considered surgical candidates. Technical improvements of resective surgery techniques facilitate successful and safe operations in highly delicate brain areas like the perisylvian area in operculoinsular epilepsy. Novel less-invasive surgical techniques include stereotactic radiosurgery, MR-guided laser interstitial thermal therapy, and stereotactic intracerebral EEG-guided radiofrequency thermocoagulation.
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Affiliation(s)
- Christoph Baumgartner
- Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria.,Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria.,Medical Faculty, Sigmund Freud University, Vienna, Austria
| | - Johannes P Koren
- Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria.,Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria
| | - Martha Britto-Arias
- Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria.,Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria
| | - Lea Zoche
- Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria.,Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria
| | - Susanne Pirker
- Department of Neurology, General Hospital Hietzing with Neurological Center Rosenhügel, Vienna, Austria.,Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria
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22
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Carboni M, Rubega M, Iannotti GR, De Stefano P, Toscano G, Tourbier S, Pittau F, Hagmann P, Momjian S, Schaller K, Seeck M, Michel CM, van Mierlo P, Vulliemoz S. The network integration of epileptic activity in relation to surgical outcome. Clin Neurophysiol 2019; 130:2193-2202. [PMID: 31669753 DOI: 10.1016/j.clinph.2019.09.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 08/21/2019] [Accepted: 09/12/2019] [Indexed: 11/24/2022]
Abstract
OBJECTIVE Epilepsy is a network disease with epileptic activity and cognitive impairment involving large-scale brain networks. A complex network is involved in the seizure and in the interictal epileptiform discharges (IEDs). Directed connectivity analysis, describing the information transfer between brain regions, and graph analysis are applied to high-density EEG to characterise networks. METHODS We analysed 19 patients with focal epilepsy who had high-density EEG containing IED and underwent surgery. We estimated cortical activity during IED using electric source analysis in 72 atlas-based cortical regions of the individual brain MRI. We applied directed connectivity analysis (information Partial Directed Coherence) and graph analysis on these sources and compared patients with good vs poor post-operative outcome at global, hemispheric and lobar level. RESULTS We found lower network integration reflected by global, hemispheric, lobar efficiency during the IED (p < 0.05) in patients with good post-surgical outcome, compared to patients with poor outcome. Prediction was better than using the IED field or the localisation obtained by electric source imaging. CONCLUSIONS Abnormal network patterns in epilepsy are related to seizure outcome after surgery. SIGNIFICANCE Our finding may help understand networks related to a more "isolated" epileptic activity, limiting the extent of the epileptic network in patients with subsequent good post-operative outcome.
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Affiliation(s)
- M Carboni
- EEG and Epilepsy, Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland; Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland.
| | - M Rubega
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - G R Iannotti
- EEG and Epilepsy, Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland; Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland; Department of Neurosurgery, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - P De Stefano
- EEG and Epilepsy, Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - G Toscano
- EEG and Epilepsy, Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland; Unit of Sleep Medicine and Epilepsy, C. Mondino National Neurological Institute, Pavia, Italy
| | - S Tourbier
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne, Lausanne, Switzerland
| | - F Pittau
- EEG and Epilepsy, Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - P Hagmann
- Connectomics Lab, Department of Radiology, University Hospital of Lausanne, Lausanne, Switzerland
| | - S Momjian
- Department of Neurosurgery, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - K Schaller
- Department of Neurosurgery, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - M Seeck
- EEG and Epilepsy, Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - C M Michel
- Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Geneva, Switzerland
| | - P van Mierlo
- EEG and Epilepsy, Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland; Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - S Vulliemoz
- EEG and Epilepsy, Neuroscience Department, University Hospital and Faculty of Medicine of Geneva, Geneva, Switzerland.
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23
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High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease. ELECTRONICS 2019. [DOI: 10.3390/electronics8091031] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Alzheimer’s Disease (AD) is a neurological disorder characterized by a progressive deterioration of brain functions that affects, above all, older adults. It can be difficult to make an early diagnosis because its first symptoms are often associated with normal aging. Electroencephalography (EEG) can be used for evaluating the loss of brain functional connectivity in AD patients. The purpose of this paper is to study the brain network parameters through the estimation of Lagged Linear Connectivity (LLC), computed by eLORETA software, applied to High-Density EEG (HD-EEG) for 84 regions of interest (ROIs). The analysis involved three groups of subjects: 10 controls (CNT), 21 Mild Cognitive Impairment patients (MCI) and 9 AD patients. In particular, the purpose is to compare the results obtained using a 256-channel EEG, the corresponding 10–10 system 64-channel EEG and the corresponding 10–20 system 18-channel EEG, both of which are extracted from the 256-electrode configuration. The computation of the Characteristic Path Length, the Clustering Coefficient, and the Connection Density from HD-EEG configuration reveals a weakening of small-world properties of MCI and AD patients in comparison to healthy subjects. On the contrary, the variation of the network parameters was not detected correctly when we employed the standard 10–20 configuration. Only the results from HD-EEG are consistent with the expected behavior of the AD brain network.
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24
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van Mierlo P, Höller Y, Focke NK, Vulliemoz S. Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity. Front Neurol 2019; 10:721. [PMID: 31379703 PMCID: PMC6651209 DOI: 10.3389/fneur.2019.00721] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 06/18/2019] [Indexed: 12/17/2022] Open
Abstract
The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience.
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Affiliation(s)
- Pieter van Mierlo
- Medical Image and Signal Processing Group, Ghent University, Ghent, Belgium
| | - Yvonne Höller
- Faculty of Psychology, University of Akureyri, Akureyri, Iceland
| | - Niels K Focke
- Clinical Neurophysiology, University Medicine Göttingen, Göttingen, Germany
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland
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25
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Technical Description of Long-Term High-Density EEG Monitoring Using 128-Channel Cap Applied With a Conductive Paste. J Clin Neurophysiol 2019; 36:175-180. [PMID: 30694941 DOI: 10.1097/wnp.0000000000000557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE EEG is a common diagnostic tool to localize epileptic activity with excellent temporal resolution and, however, with relatively low spatial resolution. High-density EEG recording is limited in clinical practice, mainly because of electrode placement difficulties, need of high technical skills, and advanced equipment requirement. METHODS We described the technique of long-term EEG recording using a 128-channel neoprene cap placed with a dielectric paste in 7 patients with refractory epilepsy. We captured electrographic seizures in six of seven patients. The 128-channel EEG cap was well tolerated except for a mild headache. Daily impedance checks and reapplication of the high impedance leads maintained the recording with impedances below 10 kΩ. RESULTS Successful long-term recording of high-density EEG was able to capture seizures in six of seven patients. The time needed to apply the electrodes was approximately 1 hour and approximately 30 minutes daily for maintenance. The EEG source localization was obtained in six of seven patients, concordant within the sublobar region for both standard and high-density EEG recordings. Three patients reported a mild headache not leading to discontinuation of the recording. CONCLUSIONS In general, long-term high-density scalp EEG recording with a dielectric paste is well tolerated and allows capturing both interictal and ictal data for localization. This small sample does not show a significant advantage in terms of sublobar localization when high-density EEG source is compared with standard 10 to 20 placement as long as the subtemporal areas are recorded.
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Michel CM, Brunet D. EEG Source Imaging: A Practical Review of the Analysis Steps. Front Neurol 2019; 10:325. [PMID: 31019487 PMCID: PMC6458265 DOI: 10.3389/fneur.2019.00325] [Citation(s) in RCA: 281] [Impact Index Per Article: 56.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Accepted: 03/15/2019] [Indexed: 11/13/2022] Open
Abstract
The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.
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Affiliation(s)
- Christoph M. Michel
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging Lausanne-Geneva (CIBM), Geneva, Switzerland
| | - Denis Brunet
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging Lausanne-Geneva (CIBM), Geneva, Switzerland
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Qin Y, Jiang S, Zhang Q, Dong L, Jia X, He H, Yao Y, Yang H, Zhang T, Luo C, Yao D. BOLD-fMRI activity informed by network variation of scalp EEG in juvenile myoclonic epilepsy. NEUROIMAGE-CLINICAL 2019; 22:101759. [PMID: 30897433 PMCID: PMC6425117 DOI: 10.1016/j.nicl.2019.101759] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 02/22/2019] [Accepted: 03/10/2019] [Indexed: 01/14/2023]
Abstract
Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME.
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Affiliation(s)
- Yun Qin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Sisi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Qiqi Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xiaoyan Jia
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Yutong Yao
- Faculty of natural science, University of Stirling, Stirling, United Kingdom
| | - Huanghao Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Tao Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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Electroencephalography, magnetoencephalography and source localization: their value in epilepsy. Curr Opin Neurol 2019; 31:176-183. [PMID: 29432218 DOI: 10.1097/wco.0000000000000545] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
PURPOSE OF REVIEW Source localization of cerebral activity using electroencephalography (EEG) or magnetoencephalography (MEG) can reveal noninvasively the generators of the abnormal signals recorded in epilepsy, such as interictal epileptic discharges (IEDs) and seizures. Here, we review recent progress showcasing the usefulness of these techniques in treating patients with drug-resistant epilepsy. RECENT FINDINGS The source localization of IEDs by high-density EEG and MEG has now been proved in large patient cohorts to be accurate and clinically relevant, with positive and negative predictive values rivaling those of structural MRI. Localizing seizure onsets is an emerging technique that seems to perform similarly well to the localization of interictal spikes, although there remain questions regarding the processing of signals for reliable results. The localization of somatosensory cortex using EEG/MEG is well established. The localization of language cortex is less reliable, although progress has been made regarding hemispheric lateralization. Source localization is also able to reveal how epilepsy alters the dynamics of neuronal activity in the large-scale networks that underlie cerebral function. SUMMARY Given the high performance of EEG/MEG source localization, these tools should find a place similar to that of established techniques like MRI in the assessment of patients for epilepsy surgery.
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Kouti M, Ansari-Asl K, Namjoo E. Epileptic source connectivity analysis based on estimating of dynamic time series of regions of interest. NETWORK (BRISTOL, ENGLAND) 2019; 30:1-30. [PMID: 31240983 DOI: 10.1080/0954898x.2019.1634290] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 04/30/2019] [Accepted: 06/17/2019] [Indexed: 06/09/2023]
Abstract
We propose a new source connectivity method by focusing on estimating time courses of the regions of interest (ROIs). To this aim, it is necessary to consider the strong inherent non-stationary behavior of neural activity. We develop an iterative dynamic approach to extract a single time course for each ROI encoding the temporal non-stationary features. The proposed approach explicitly includes dynamic constraints by taking into account the evolution of the sources activities for further dynamic connectivity analysis. We simulated an epileptic network with a non-stationary structure; accordingly, EEG source reconstruction using LORETA is performed. Using the reconstructed sources, the spatially compact ROIs are selected. Then, a single time course encoding the temporal non-stationarity is extracted for each ROI. An adaptive directed transfer function (ADTF) is applied to measure the information flow of underlying brain networks. Obtained results demonstrate that the contributed approach is more efficient to estimate the ROI time series and ROI to ROI information flow in comparison with existing methods. Our work is validated in three drug-resistance epilepsy patients. The proposed ROI time series estimation directly affects the quality of connectivity analysis, leading to the best possible seizure onset zone (SOZ) localization verified by electrocorticography and post-operational results.
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Affiliation(s)
- Mayadeh Kouti
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Karim Ansari-Asl
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Ehsan Namjoo
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
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30
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31
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Baroumand AG, van Mierlo P, Strobbe G, Pinborg LH, Fabricius M, Rubboli G, Leffers AM, Uldall P, Jespersen B, Brennum J, Henriksen OM, Beniczky S. Automated EEG source imaging: A retrospective, blinded clinical validation study. Clin Neurophysiol 2018; 129:2403-2410. [DOI: 10.1016/j.clinph.2018.09.015] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 08/21/2018] [Accepted: 09/15/2018] [Indexed: 11/16/2022]
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32
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Nissen IA, Stam CJ, van Straaten ECW, Wottschel V, Reijneveld JC, Baayen JC, de Witt Hamer PC, Idema S, Velis DN, Hillebrand A. Localization of the Epileptogenic Zone Using Interictal MEG and Machine Learning in a Large Cohort of Drug-Resistant Epilepsy Patients. Front Neurol 2018; 9:647. [PMID: 30131762 PMCID: PMC6090046 DOI: 10.3389/fneur.2018.00647] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 07/18/2018] [Indexed: 01/01/2023] Open
Abstract
Objective: Epilepsy surgery results in seizure freedom in the majority of drug-resistant patients. To improve surgery outcome we studied whether MEG metrics combined with machine learning can improve localization of the epileptogenic zone, thereby enhancing the chance of seizure freedom. Methods: Presurgical interictal MEG recordings of 94 patients (64 seizure-free >1y post-surgery) were analyzed to extract four metrics in source space: delta power, low-to-high-frequency power ratio, functional connectivity (phase lag index), and minimum spanning tree betweenness centrality. At the group level, we estimated the overlap of the resection area with the five highest values for each metric and determined whether this overlap differed between surgery outcomes. At the individual level, those metrics were used in machine learning classifiers (linear support vector machine (SVM) and random forest) to distinguish between resection and non-resection areas and between surgery outcome groups. Results: The highest values, for all metrics, overlapped with the resection area in more than half of the patients, but the overlap did not differ between surgery outcome groups. The classifiers distinguished the resection areas from non-resection areas with 59.94% accuracy (95% confidence interval: 59.67–60.22%) for SVM and 60.34% (59.98–60.71%) for random forest, but could not differentiate seizure-free from not seizure-free patients [43.77% accuracy (42.08–45.45%) for SVM and 49.03% (47.25–50.82%) for random forest]. Significance: All four metrics localized the resection area but did not distinguish between surgery outcome groups, demonstrating that metrics derived from interictal MEG correspond to expert consensus based on several presurgical evaluation modalities, but do not yet localize the epileptogenic zone. Metrics should be improved such that they correspond to the resection area in seizure-free patients but not in patients with persistent seizures. It is important to test such localization strategies at an individual level, for example by using machine learning or individualized models, since surgery is individually tailored.
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Affiliation(s)
- Ida A Nissen
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
| | - Viktor Wottschel
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands
| | - Jaap C Reijneveld
- Brain Tumor Center Amsterdam & Department of Neurology, VU University Medical Center, Amsterdam, Netherlands
| | - Johannes C Baayen
- Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Philip C de Witt Hamer
- Brain Tumor Center Amsterdam & Department of Neurology, VU University Medical Center, Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Sander Idema
- Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Demetrios N Velis
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, Netherlands
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Feng R, Hu J, Wu J, Lang L, Ma C, Sun B, Gu X, Pan L. Accurate source imaging based on high resolution scalp electroencephalography and individualized finite difference head models in epilepsy pre-surgical workup. Seizure 2018; 59:126-131. [DOI: 10.1016/j.seizure.2018.05.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 05/12/2018] [Accepted: 05/15/2018] [Indexed: 10/16/2022] Open
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van Mierlo P, Lie O, Staljanssens W, Coito A, Vulliémoz S. Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG. Brain Topogr 2018; 31:753-766. [PMID: 29700719 PMCID: PMC6097740 DOI: 10.1007/s10548-018-0646-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Accepted: 04/18/2018] [Indexed: 10/28/2022]
Abstract
We investigated the influence of processing steps in the estimation of multivariate directed functional connectivity during seizures recorded with intracranial EEG (iEEG) on seizure-onset zone (SOZ) localization. We studied the effect of (i) the number of nodes, (ii) time-series normalization, (iii) the choice of multivariate time-varying connectivity measure: Adaptive Directed Transfer Function (ADTF) or Adaptive Partial Directed Coherence (APDC) and (iv) graph theory measure: outdegree or shortest path length. First, simulations were performed to quantify the influence of the various processing steps on the accuracy to localize the SOZ. Afterwards, the SOZ was estimated from a 113-electrodes iEEG seizure recording and compared with the resection that rendered the patient seizure-free. The simulations revealed that ADTF is preferred over APDC to localize the SOZ from ictal iEEG recordings. Normalizing the time series before analysis resulted in an increase of 25-35% of correctly localized SOZ, while adding more nodes to the connectivity analysis led to a moderate decrease of 10%, when comparing 128 with 32 input nodes. The real-seizure connectivity estimates localized the SOZ inside the resection area using the ADTF coupled to outdegree or shortest path length. Our study showed that normalizing the time-series is an important pre-processing step, while adding nodes to the analysis did only marginally affect the SOZ localization. The study shows that directed multivariate Granger-based connectivity analysis is feasible with many input nodes (> 100) and that normalization of the time-series before connectivity analysis is preferred.
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Affiliation(s)
- Pieter van Mierlo
- Medical Image and Signal Processing Group, Ghent University, Ghent, Belgium. .,Functional Brain Mapping Lab, University of Geneva, Geneva, Switzerland. .,EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland.
| | - Octavian Lie
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | | | - Ana Coito
- Functional Brain Mapping Lab, University of Geneva, Geneva, Switzerland
| | - Serge Vulliémoz
- Functional Brain Mapping Lab, University of Geneva, Geneva, Switzerland.,EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland
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35
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Staljanssens W, Strobbe G, Van Holen R, Keereman V, Gadeyne S, Carrette E, Meurs A, Pittau F, Momjian S, Seeck M, Boon P, Vandenberghe S, Vulliemoz S, Vonck K, van Mierlo P. EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy. NEUROIMAGE-CLINICAL 2017; 16:689-698. [PMID: 29034162 PMCID: PMC5633847 DOI: 10.1016/j.nicl.2017.09.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 09/01/2017] [Accepted: 09/12/2017] [Indexed: 11/25/2022]
Abstract
Electrical source imaging (ESI) from interictal scalp EEG is increasingly validated and used as a valuable tool in the presurgical evaluation of epilepsy as a reflection of the irritative zone. ESI of ictal scalp EEG to localize the seizure onset zone (SOZ) remains challenging. We investigated the value of an approach for ictal imaging using ESI and functional connectivity analysis (FC). Ictal scalp EEG from 111 seizures in 27 patients who had Engel class I outcome at least 1 year following resective surgery was analyzed. For every seizure, an artifact-free epoch close to the seizure onset was selected and ESI using LORETA was applied. In addition, the reconstructed sources underwent FC using the spectrum-weighted Adaptive Directed Transfer Function. This resulted in the estimation of the SOZ in two ways: (i) the source with maximal power after ESI, (ii) the source with the strongest outgoing connections after combined ESI and FC. Next, we calculated the distance between the estimated SOZ and the border of the resected zone (RZ) for both approaches and called this the localization error ((i) LEpow and (ii) LEconn respectively). By comparing LEpow and LEconn, we assessed the added value of FC. The source with maximal power after ESI was inside the RZ (LEpow = 0 mm) in 31% of the seizures and estimated within 10 mm from the border of the RZ (LEpow ≤ 10 mm) in 42%. Using ESI and FC, these numbers increased to 72% for LEconn = 0 mm and 94% for LEconn ≤ 10 mm. FC provided a significant added value to ESI alone (p < 0.001). ESI combined with subsequent FC is able to localize the SOZ in a non-invasive way with high accuracy. Therefore it could be a valuable tool in the presurgical evaluation of epilepsy. ESI + functional connectivity analysis allows localizing the SOZ with high accuracy. Functional connectivity analysis offered a significant added value to ESI. The method is robust for inter- and intra-patient variability. The method could be a useful tool in the presurgical evaluation of epilepsy.
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Affiliation(s)
- Willeke Staljanssens
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium
| | | | - Roel Van Holen
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium
| | - Vincent Keereman
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium.,Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Stefanie Gadeyne
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Evelien Carrette
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Alfred Meurs
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Francesca Pittau
- EEG and Epilepsy Unit, Neurology Department, University Hospitals and Faculty of Medicine of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland
| | - Shahan Momjian
- Department of Neurosurgery, University Hospitals of Geneva and University of Geneva, rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland
| | - Margitta Seeck
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Paul Boon
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Stefaan Vandenberghe
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, Neurology Department, University Hospitals and Faculty of Medicine of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.,Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland
| | - Kristl Vonck
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Department of Neurology, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium
| | - Pieter van Mierlo
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University - imec, De Pintelaan 185, 9000 Ghent, Belgium.,Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland
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36
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Martinez-Vargas JD, Strobbe G, Vonck K, van Mierlo P, Castellanos-Dominguez G. Improved Localization of Seizure Onset Zones Using Spatiotemporal Constraints and Time-Varying Source Connectivity. Front Neurosci 2017; 11:156. [PMID: 28428738 PMCID: PMC5382162 DOI: 10.3389/fnins.2017.00156] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 03/10/2017] [Indexed: 11/30/2022] Open
Abstract
Presurgical evaluation of brain neural activity is commonly carried out in refractory epilepsy patients to delineate as accurately as possible the seizure onset zone (SOZ) before epilepsy surgery. In practice, any subjective interpretation of electroencephalographic (EEG) recordings is hindered mainly because of the highly stochastic behavior of the epileptic activity. We propose a new method for dynamic source connectivity analysis that aims to accurately localize the seizure onset zones by explicitly including temporal, spectral, and spatial information of the brain neural activity extracted from EEG recordings. In particular, we encode the source nonstationarities in three critical stages of processing: Inverse problem solution, estimation of the time courses extracted from the regions of interest, and connectivity assessment. With the aim to correctly encode all temporal dynamics of the seizure-related neural network, a directed functional connectivity measure is employed to quantify the information flow variations over the time window of interest. Obtained results on simulated and real EEG data confirm that the proposed approach improves the accuracy of SOZ localization.
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Affiliation(s)
- Juan D Martinez-Vargas
- Signal Processing and Recognition Group, Universidad Nacional de ColombiaManizales, Colombia
| | - Gregor Strobbe
- Medical Image and Signal Processing Group, iMinds Medical IT Department, Ghent UniversityGhent, Belgium
| | - Kristl Vonck
- Laboratory for Clinical and Experimental Neurophysiology, Neurobiology and Neuropsychology, Ghent University HospitalGhent, Belgium
| | - Pieter van Mierlo
- Medical Image and Signal Processing Group, iMinds Medical IT Department, Ghent UniversityGhent, Belgium
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