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Carboni M, Brunet D, Seeber M, Michel CM, Vulliemoz S, Vorderwülbecke BJ. Linear distributed inverse solutions for interictal EEG source localisation. Clin Neurophysiol 2021; 133:58-67. [PMID: 34801964 DOI: 10.1016/j.clinph.2021.10.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 09/27/2021] [Accepted: 10/09/2021] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To compare the spatial accuracy of 6 linear distributed inverse solutions for EEG source localisation of interictal epileptic discharges: Minimum Norm, Weighted Minimum Norm, Low-Resolution Electromagnetic Tomography (LORETA), Local Autoregressive Average (LAURA), Standardised LORETA, and Exact LORETA. METHODS Spatial accuracy was assessed clinically by retrospectively comparing the maximum source of averaged interictal discharges to the resected brain area in 30 patients with successful epilepsy surgery, based on 204-channel EEG. Additionally, localisation errors of the inverse solutions were assessed in computer simulations, with different levels of noise added to the signal in both sensor space and source space. RESULTS In the clinical evaluations, the source maximum was located inside the resected brain area in 50-57% of patients when using LORETA or LAURA, while all other inverse solutions performed significantly worse (17-30%; corrected p < 0.01). In the simulation studies, when noise levels exceeded 10%, LORETA and LAURA had substantially smaller localisation errors than the other inverse solutions. CONCLUSIONS LORETA and LAURA provided the highest spatial accuracy both in clinical and simulated data, alongside with a comparably high robustness towards noise. SIGNIFICANCE Among the different linear inverse solution algorithms tested, LORETA and LAURA might be preferred for interictal EEG source localisation.
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Affiliation(s)
- 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
| | - 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
| | - Christoph M Michel
- Functional Brain Mapping Lab, Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, 1202 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
| | - Bernd J Vorderwülbecke
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland; Epilepsy-Center Berlin-Brandenburg, Department of Neurology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
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2
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Vorderwülbecke BJ, Baroumand AG, Spinelli L, Seeck M, van Mierlo P, Vulliémoz S. Automated interictal source localisation based on high-density EEG. Seizure 2021; 92:244-251. [PMID: 34626920 DOI: 10.1016/j.seizure.2021.09.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/25/2021] [Accepted: 09/29/2021] [Indexed: 11/26/2022] Open
Abstract
PURPOSE To study the accuracy of automated interictal EEG source localisation based on high-density EEG, and to compare it to low-density EEG. METHODS Thirty patients operated for pharmacoresistant focal epilepsy were retrospectively examined. Twelve months after resective brain surgery, 18 were seizure-free or had 'auras' only, while 12 had persistence of disabling seizures. Presurgical 257-channel EEG lasting 3-20 h was down-sampled to 25, 40, and 204 channels for separate analyses. For each electrode setup, interictal spikes were detected, clustered, and averaged automatically before validation by an expert reviewer. An individual 6-layer finite difference head model and the standardised low-resolution electromagnetic tomography were used to localise the maximum source activity of the most prevalent spike. Sublobar concordance with the resected brain area was visually assessed and related to favourable vs. unfavourable postsurgical outcome. RESULTS Depending on the EEG setup, epileptic spikes were detected in 21-24 patients (70-80%). The median number of single spikes per average was 470 (range 17-15,066). Diagnostic sensitivity of EEG source localisation was 58-75%, specificity was 50-67%, and overall accuracy was 55-71%. There were no significant differences between low- and high-density EEG setups with 25 to 257 electrodes. CONCLUSION Automated high-density EEG source localisation provides meaningful information in the majority of cases. With hundreds of single spikes averaged, diagnostic accuracy is similar in high- and low-density EEG. Therefore, low-density EEG may be sufficient for interictal EEG source localisation if high numbers of spikes are available.
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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; Epilepsy-Center Berlin-Brandenburg, Department of Neurology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
| | - Amir G Baroumand
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium; Epilog NV, Vlasgaardstraat 52, 9000 Ghent, Belgium
| | - 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
| | - Pieter van Mierlo
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium; Epilog NV, Vlasgaardstraat 52, 9000 Ghent, Belgium
| | - Serge Vulliémoz
- 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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3
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Baroumand AG, Arbune AA, Strobbe G, Keereman V, Pinborg LH, Fabricius M, Rubboli G, Gøbel Madsen C, Jespersen B, Brennum J, Mølby Henriksen O, Mierlo PV, Beniczky S. Automated ictal EEG source imaging: A retrospective, blinded clinical validation study. Clin Neurophysiol 2021; 141:119-125. [PMID: 33972159 DOI: 10.1016/j.clinph.2021.03.040] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/23/2021] [Accepted: 03/02/2021] [Indexed: 11/03/2022]
Abstract
OBJECTIVE EEG source imaging (ESI) is a validated tool in the multimodal workup of patients with drug resistant focal epilepsy. However, it requires special expertise and it is underutilized. To circumvent this, automated analysis pipelines have been developed and validated for the interictal discharges. In this study, we present the clinical validation of an automated ESI for ictal EEG signals. METHODS We have developed an automated analysis pipeline of ictal EEG activity, based on spectral analysis in source space, using an individual head model of six tissues. The analysis was done blinded to all other data. As reference standard, we used the concordance with the resected area and one-year postoperative outcome. RESULTS We analyzed 50 consecutive patients undergoing epilepsy surgery (34 temporal and 16 extra-temporal). Thirty patients (60%) became seizure-free. The accuracy of the automated ESI was 74% (95% confidence interval: 59.66-85.37%). CONCLUSIONS Automated ictal ESI has a high accuracy for localizing the seizure onset zone. SIGNIFICANCE Automating the ESI of the ictal EEG signals will facilitate implementation of this tool in the presurgical evaluation.
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Affiliation(s)
- Amir G Baroumand
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Campus UZ Corneel Heymanslaan 10, 9000 Ghent, Belgium; Epilog NV, Vlasgaardstraat 52, 9000 Ghent, Belgium
| | - Anca A Arbune
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Visby Allé 5, 4293 Dianalund, Denmark; Neurology Clinic, Fundeni Clinical Institute, Soseaua Fundeni no. 258, Sector 2, 022328 Bucharest, Romania
| | | | - Vincent Keereman
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Campus UZ Corneel Heymanslaan 10, 9000 Ghent, Belgium; Epilog NV, Vlasgaardstraat 52, 9000 Ghent, Belgium
| | - Lars H Pinborg
- Department of Neurology, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark; Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, 9 Blegdamsvej, DK-2100 Copenhagen Ø, Denmark
| | - Martin Fabricius
- Department of Clinical Neurophysiology, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
| | - Guido Rubboli
- Department of Neurology, Danish Epilepsy Centre, Kolonivej 1, 4293 Dianalund, Denmark; Institute of Clinical Medicine, University of Copenhagen, Denmark
| | - Camilla Gøbel Madsen
- Department of Diagnostic Radiology, Centre for Functional and Diagnostic Imaging and Research, Hvidovre Hospital, Kettegaard Alle 30, 2650 Hvidovre, Denmark
| | - Bo Jespersen
- Department of Neurosurgery, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
| | - Jannick Brennum
- Department of Neurosurgery, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
| | - Otto Mølby Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
| | - Pieter van Mierlo
- Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Campus UZ Corneel Heymanslaan 10, 9000 Ghent, Belgium; Epilog NV, Vlasgaardstraat 52, 9000 Ghent, Belgium
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Visby Allé 5, 4293 Dianalund, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, Palle Juul-Jensens Blvd., 8200 Aarhus, Denmark.
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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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5
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Moffet EW, Verhagen R, Jones B, Findlay G, Juan E, Bugnon T, Mensen A, Aparicio MK, Maganti R, Struck AF, Tononi G, Boly M. Local Sleep Slow-Wave Activity Colocalizes With the Ictal Symptomatogenic Zone in a Patient With Reflex Epilepsy: A High-Density EEG Study. Front Syst Neurosci 2020; 14:549309. [PMID: 33192347 PMCID: PMC7609881 DOI: 10.3389/fnsys.2020.549309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 09/17/2020] [Indexed: 11/21/2022] Open
Abstract
Background: Slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep reflects synaptic potentiation during preceding wakefulness. Epileptic activity may induce increases in state-dependent SWA in human brains, therefore, localization of SWA may prove useful in the presurgical workup of epileptic patients. We analyzed high-density electroencephalography (HDEEG) data across vigilance states from a reflex epilepsy patient with a clearly localizable ictal symptomatogenic zone to provide a proof-of-concept for the testability of this hypothesis. Methods: Overnight HDEEG recordings were obtained in the patient during REM sleep, NREM sleep, wakefulness, and during a right facial motor seizure then compared to 10 controls. After preprocessing, SWA (i.e., delta power; 1–4 Hz) was calculated at each channel. Scalp level and source reconstruction analyses were computed. We assessed for statistical differences in maximum SWA between the patient and controls within REM sleep, NREM sleep, wakefulness, and seizure. Then, we completed an identical statistical comparison after first subtracting intrasubject REM sleep SWA from that of NREM sleep, wakefulness, and seizure SWA. Results: The topographical analysis revealed greater left hemispheric SWA in the patient vs. controls in all vigilance states except REM sleep (which showed a right hemispheric maximum). Source space analysis revealed increased SWA in the left inferior frontal cortex during NREM sleep and wakefulness. Ictal data displayed poor source-space localization. Comparing each state to REM sleep enhanced localization accuracy; the most clearly localizing results were observed when subtracting REM sleep from wakefulness. Conclusion: State-dependent SWA during NREM sleep and wakefulness may help to identify aspects of the potential epileptogenic zone. Future work in larger cohorts may assess the clinical value of sleep SWA to help presurgical planning.
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Affiliation(s)
- Eric W Moffet
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Ruben Verhagen
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States.,Department of Philosophy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Benjamin Jones
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Graham Findlay
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Elsa Juan
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States.,Department of Philosophy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Tom Bugnon
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Armand Mensen
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | | | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States
| | - Giulio Tononi
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Melanie Boly
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, United States.,Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
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6
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Kowalczyk MA, Omidvarnia A, Dhollander T, Jackson GD. Dynamic analysis of fMRI activation during epileptic spikes can help identify the seizure origin. Epilepsia 2020; 61:2558-2571. [PMID: 32954506 DOI: 10.1111/epi.16695] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 08/25/2020] [Accepted: 08/25/2020] [Indexed: 11/29/2022]
Abstract
OBJECTIVE We use the dynamic electroencephalography-functional magnetic resonance imaging (EEG-fMRI) method to incorporate variability in the amplitude and field of the interictal epileptic discharges (IEDs) into the fMRI analysis. We ask whether IED variability analysis can (a) identify additional activated brain regions during the course of IEDs, not seen in standard analysis; and (b) demonstrate the origin and spread of epileptic activity. We explore whether these functional changes recapitulate the structural connections and propagation of epileptic activity during seizures. METHODS Seventeen patients with focal epilepsy and at least 30 IEDs of a single type during simultaneous EEG-fMRI were studied. IED variability and EEG source imaging (ESI) analysis extracted time-varying dynamic changes. General linear modeling (GLM) generated static functional maps. Dynamic maps were compared to static functional maps. The dynamic sequence from IED variability was compared to the ESI results. In a subset of patients, we investigated structural connections between active brain regions using diffusion-based fiber tractography. RESULTS IED variability distinguished the origin of epileptic activity from its propagation in 15 of 17 (88%) patients. This included two cases where no result was obtained from the standard GLM analysis. In both of these cases, IED variability revealed activation in line with the presumed epileptic focus. Two cases showed no result from either method. Both had very high spike rates associated with dysplasia in the postcentral gyrus. In all 15 cases with dynamic activation, the observed dynamics were concordant with ESI. Fiber tractography identified specific white matter pathways between brain regions that were active at IED onset and propagation. SIGNIFICANCE Dynamic techniques involving IED variability can provide additional power for EEG-fMRI analysis, compared to standard analysis, revealing additional biologically plausible information in cases with no result from the standard analysis and gives insight into the origin and spread of IEDs.
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Affiliation(s)
- Magdalena A Kowalczyk
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne Vic., Australia
| | - Amir Omidvarnia
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne Vic., Australia.,Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Campus Biotech, Geneva, Switzerland.,Department of Radiology and Medical Informatics, Campus Biotech, University of Geneva, Geneva, Switzerland
| | - Thijs Dhollander
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne Vic., Australia.,Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Vic., Australia
| | - Graeme D Jackson
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne Vic., Australia.,Department of Neurology, Austin Health, Heidelberg, Vic., Australia
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7
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Asadzadeh S, Yousefi Rezaii T, Beheshti S, Delpak A, Meshgini S. A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities. J Neurosci Methods 2020; 339:108740. [DOI: 10.1016/j.jneumeth.2020.108740] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 04/13/2020] [Accepted: 04/13/2020] [Indexed: 12/12/2022]
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8
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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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9
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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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10
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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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11
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Kim SH, Lim SC, Yang DW, Cho JH, Son BC, Kim J, Hong SB, Shon YM. Thalamo-cortical network underlying deep brain stimulation of centromedian thalamic nuclei in intractable epilepsy: a multimodal imaging analysis. Neuropsychiatr Dis Treat 2017; 13:2607-2619. [PMID: 29089767 PMCID: PMC5655132 DOI: 10.2147/ndt.s148617] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE Deep brain stimulation (DBS) of the centromedian thalamic nucleus (CM) can be an alternative treatment option for intractable epilepsy patients. Since CM may be involved in widespread cortico-subcortical networks, identification of the cortical sub-networks specific to the target stimuli may provide further understanding on the underlying mechanisms of CM DBS. Several brain structures have distinguishing brain connections that may be related to the pivotal propagation and subsequent clinical effect of DBS. METHODS To explore core structures and their connections relevant to CM DBS, we applied electroencephalogram (EEG) and diffusion tensor imaging (DTI) to 10 medically intractable patients - three generalized epilepsy (GE) and seven multifocal epilepsy (MFE) patients unsuitable for resective surgery. Spatiotemporal activation pattern was mapped from scalp EEG by delivering low-frequency stimuli (5 Hz). Structural connections between the CM and the cortical activation spots were assessed using DTI. RESULTS We confirmed an average 72% seizure reduction after CM DBS and its clinical efficiency remained consistent during the observation period (mean 21 months). EEG data revealed sequential source propagation from the anterior cingulate, followed by the frontotemporal regions bilaterally. In addition, maximal activation was found in the left cingulate gyrus and the right medial frontal cortex during the right and left CM stimulation, respectively. From DTI data, we confirmed concrete structural connections between CM and those maximal activation spots identified from EEG data. CONCLUSION These results suggest that the anterior cingulate can be a core cortical structure for the bilateral propagation of CM stimulation. Our DTI findings also indicate that the propagation of CM stimulation may rely upon integrity of structural connections between CM and these key cortical regions. Structures and their connections found in this study may be relevant in the interpretation of the clinical outcomes of CM DBS.
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Affiliation(s)
| | | | | | | | - Byung-Chul Son
- Department of Neurosurgery, Catholic Neuroscience Institute, College of Medicine, The Catholic University of Korea, Seoul
| | - Jiyeon Kim
- Department of Neurology, Korea University Ansan Hospital, College of Medicine, Korea University, Ansan
| | - Seung Bong Hong
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Young-Min Shon
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data. Neuroimage 2016; 143:175-195. [DOI: 10.1016/j.neuroimage.2016.08.044] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Revised: 08/18/2016] [Accepted: 08/20/2016] [Indexed: 11/23/2022] Open
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13
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Staljanssens W, Strobbe G, Holen RV, Birot G, Gschwind M, Seeck M, Vandenberghe S, Vulliémoz S, van Mierlo P. Seizure Onset Zone Localization from Ictal High-Density EEG in Refractory Focal Epilepsy. Brain Topogr 2016; 30:257-271. [PMID: 27853892 DOI: 10.1007/s10548-016-0537-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Accepted: 11/04/2016] [Indexed: 10/20/2022]
Abstract
Epilepsy surgery is the most efficient treatment option for patients with refractory epilepsy. Before surgery, it is of utmost importance to accurately delineate the seizure onset zone (SOZ). Non-invasive EEG is the most used neuroimaging technique to diagnose epilepsy, but it is hard to localize the SOZ from EEG due to its low spatial resolution and because epilepsy is a network disease, with several brain regions becoming active during a seizure. In this work, we propose and validate an approach based on EEG source imaging (ESI) combined with functional connectivity analysis to overcome these problems. We considered both simulations and real data of patients. Ictal epochs of 204-channel EEG and subsets down to 32 channels were analyzed. ESI was done using realistic head models and LORETA was used as inverse technique. The connectivity pattern between the reconstructed sources was calculated, and the source with the highest number of outgoing connections was selected as SOZ. We compared this algorithm with a more straightforward approach, i.e. selecting the source with the highest power after ESI as the SOZ. We found that functional connectivity analysis estimated the SOZ consistently closer to the simulated EZ/RZ than localization based on maximal power. Performance, however, decreased when 128 electrodes or less were used, especially in the realistic data. The results show the added value of functional connectivity analysis for SOZ localization, when the EEG is obtained with a high-density setup. Next to this, the method can potentially be used as objective tool in clinical settings.
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Affiliation(s)
- Willeke Staljanssens
- MEDISIP, Department of Electronics and Information Systems, Ghent University, De Pintelaan 185, Building B Entrance 36, 9000, Ghent, Belgium. .,iMinds Medical IT, Ghent, Belgium.
| | - Gregor Strobbe
- MEDISIP, Department of Electronics and Information Systems, Ghent University, De Pintelaan 185, Building B Entrance 36, 9000, Ghent, Belgium.,iMinds Medical IT, Ghent, Belgium
| | - Roel Van Holen
- MEDISIP, Department of Electronics and Information Systems, Ghent University, De Pintelaan 185, Building B Entrance 36, 9000, Ghent, Belgium.,iMinds Medical IT, Ghent, Belgium
| | - Gwénaël Birot
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Campus Biotech, Geneva, Switzerland
| | - Markus Gschwind
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Campus Biotech, Geneva, Switzerland.,EEG and Epilepsy Unit, Neurology Department, University Hospitals and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Margitta Seeck
- EEG and Epilepsy Unit, Neurology Department, University Hospitals and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Stefaan Vandenberghe
- MEDISIP, Department of Electronics and Information Systems, Ghent University, De Pintelaan 185, Building B Entrance 36, 9000, Ghent, Belgium.,iMinds Medical IT, Ghent, Belgium
| | - Serge Vulliémoz
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Campus Biotech, Geneva, Switzerland.,EEG and Epilepsy Unit, Neurology Department, University Hospitals and Faculty of Medicine of Geneva, Geneva, Switzerland
| | - Pieter van Mierlo
- MEDISIP, Department of Electronics and Information Systems, Ghent University, De Pintelaan 185, Building B Entrance 36, 9000, Ghent, Belgium.,iMinds Medical IT, Ghent, Belgium.,Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva, Campus Biotech, Geneva, Switzerland
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