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Liu F, Wang L, Lou Y, Li RC, Purdon PL. Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:321-334. [PMID: 32956052 DOI: 10.1109/tmi.2020.3025608] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume the source activities at different time points are unrelated, and do not utilize the temporal structure in the source activation, making the ESI analysis sensitive to noise. Some methods may encourage very similar activation patterns across the entire time course and may be incapable of accounting the variation along the time course. To effectively deal with noise while maintaining flexibility and continuity among brain activation patterns, we propose a novel probabilistic ESI model based on a hierarchical graph prior. Under our method, a spanning tree constraint ensures that activity patterns have spatiotemporal continuity. An efficient algorithm based on an alternating convex search is presented to solve the resulting problem of the proposed model with guaranteed convergence. Comprehensive numerical studies using synthetic data on a realistic brain model are conducted under different levels of signal-to-noise ratio (SNR) from both sensor and source spaces. We also examine the EEG/MEG datasets in two real applications, in which our ESI reconstructions are neurologically plausible. All the results demonstrate significant improvements of the proposed method over benchmark methods in terms of source localization performance, especially at high noise levels.
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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: 35] [Impact Index Per Article: 7.0] [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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Boom M, Raskin JS, Curry DJ, Weiner HL, Peters JM. Technological advances in pediatric epilepsy surgery: implications for tuberous sclerosis complex. FUTURE NEUROLOGY 2017. [DOI: 10.2217/fnl-2017-0005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In selected children with tuberous sclerosis complex, epilepsy surgery leads to seizure freedom or seizure reduction. The current standard involves a multimodal pre-surgical workup followed by invasive electrocorticographic monitoring and resective surgery. Recent insights in the disorder and novel technologies are changing the approach to pediatric epilepsy surgery in tuberous sclerosis complex. New evidence suggests tubers are poorly delineated, and epileptogenic activity may originate in the perituber tissue. Novel imaging modalities relevant to surgical planning include high-resolution MRI, α-methyl-l-tryptophan or fluorodeoxyglucose PET with diffusion tensor imaging. Advanced neurophysiological techniques have improved identification of the surgical target, including magnetoencephalography, electrical source imaging of high-density electroencephalograph data, and high-frequency oscillations in electrocorticography data. Simultaneously, novel surgical tools including stereo-electroencephalography and laser-induced thermal therapy have become available for children. This article reviews the literature in the light of these rapidly changing technologies.
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Affiliation(s)
- Merel Boom
- Division of Epilepsy & Clinical Neurophysiology, Boston Children’s Hospital & Harvard Medical School, 300 Longwood Avenue, BCH 3063, Boston, MA 02115, USA
| | - Jeffrey S Raskin
- Division of Pediatric Neurosurgery, Department of Surgery, Texas Children’s Hospital & Department of Neurosurgery, Baylor College of Medicine, 6701 Fannin St. Suite 1230.01, Houston, TX 77030, USA
| | - Daniel J Curry
- Division of Pediatric Neurosurgery, Department of Surgery, Texas Children’s Hospital & Department of Neurosurgery, Baylor College of Medicine, 6701 Fannin St. Suite 1230.01, Houston, TX 77030, USA
| | - Howard L Weiner
- Division of Pediatric Neurosurgery, Department of Surgery, Texas Children’s Hospital & Department of Neurosurgery, Baylor College of Medicine, 6701 Fannin St. Suite 1230.01, Houston, TX 77030, USA
| | - Jurriaan M Peters
- Division of Epilepsy & Clinical Neurophysiology, Boston Children’s Hospital & Harvard Medical School, 300 Longwood Avenue, BCH 3063, Boston, MA 02115, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School,300 Longwood Avenue, BCH 3429, Boston, MA 02115, SA
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