The interictal suppression hypothesis is the dominant differentiator of seizure onset zones in focal epilepsy.

Autor:	Doss DJ; Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA.; Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA.; Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA., Shless JS; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA., Bick SK; Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA.; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA., Makhoul GS; Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA.; Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA.; Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA., Negi AS; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA., Bibro CE; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA., Rashingkar R; Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA., Gummadavelli A; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA., Chang C; Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA.; Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA.; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA., Gallagher MJ; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA., Naftel RP; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA., Reddy SB; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA., Williams Roberson S; Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA.; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA., Morgan VL; Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA.; Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA.; Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA.; Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA.; Department of Radiology and Biomedical Imaging, Vanderbilt University Medical Center, Nashville, TN 37235, USA., Johnson GW; Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA.; Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA.; Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA., Englot DJ; Department of Biomedical Engineering, Vanderbilt University Nashville, Nashville, TN 37235, USA.; Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37235, USA.; Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University Nashville, Nashville, TN 37235, USA.; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA.; Department of Computer Science, Vanderbilt University Nashville, Nashville, TN 37235, USA.; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, USA.; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37235, USA.; Department of Radiology and Biomedical Imaging, Vanderbilt University Medical Center, Nashville, TN 37235, USA.
Jazyk:	angličtina
Zdroj:	Brain : a journal of neurology [Brain] 2024 Sep 03; Vol. 147 (9), pp. 3009-3017.
DOI:	10.1093/brain/awae189
Abstrakt:	Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8-13 Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment. (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)
Databáze:	MEDLINE
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