Interictal discharge traveling waves recorded from stereoelectroencephalography electrodes.

Autor:	Yearley AG; 1Harvard Medical School, Boston, Massachusetts.; 2Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts., Smith EH; 3Department of Neurosurgery, University of Utah, Salt Lake City, Utah., Davis TS; 3Department of Neurosurgery, University of Utah, Salt Lake City, Utah., Anderson D; 4Department of Biomedical Engineering, Faculty of Engineering, University of Sydney, Darlington, New South Wales, Australia; and., Arain AM; 5Division of Epilepsy, Department of Neurology, University of Utah School of Medicine, Salt Lake City, Utah., Rolston JD; 2Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Jazyk:	angličtina
Zdroj:	Journal of neurosurgery [J Neurosurg] 2024 May 24; Vol. 141 (4), pp. 1115-1123. Date of Electronic Publication: 2024 May 24 (Print Publication: 2024).
DOI:	10.3171/2024.3.JNS2441
Abstrakt:	Objective: Interictal epileptiform discharges (IEDs) are intermittent high-amplitude electrical signals that occur between seizures. They have been shown to propagate through the brain as traveling waves when recorded with epicortical grid-type electrodes and small penetrating microelectrode arrays. However, little work has been done to translate experimental IED analyses to more clinically relevant platforms such as stereoelectroencephalography (SEEG). In this pilot study, the authors aimed to define a computational method to identify and characterize IEDs recorded from clinical SEEG electrodes and leverage the directionality of IED traveling waves to localize the seizure onset zone (SOZ). Methods: Continuous SEEG recordings from 15 patients with medically refractory epilepsy were collected, and IEDs were detected by identifying overlapping peaks of a minimum prominence. IED pathways of propagation were defined and compared to the SOZ location determined by a clinical neurologist based on the ictal recordings. For further analysis of the IED pathways of propagation, IED detections were divided into triplets, defined as a set of 3 consecutive contacts within the same IED detection. Univariate and multivariate linear regression models were employed to associate IED characteristics with colocalization to the SOZ. Results: A median (range) of 22.6 (4.4-183.9) IEDs were detected per hour from 15 patients over a mean of 23.2 hours of recording. Depending on the definition of the SOZ, a median (range) of 20.8% (0.0%-54.5%) to 62.1% (19.2%-99.4%) of IEDs per patient traversed the SOZ. IEDs passing through the SOZ followed discrete pathways that had little overlap with those of the IEDs passing outside the SOZ. Contact triplets that occurred more than once were significantly more likely to be detected in an IED passing through the SOZ (p < 0.001). Per our multivariate model, patients with a greater proportion of IED traveling waves had a significantly greater proportion of IEDs that localized to the SOZ (β = 0.64, 95% CI 0.01-1.27, p = 0.045). Conclusions: By using computational methods, IEDs can be meaningfully detected from clinical-grade SEEG recordings of patients with epilepsy. In some patients, a high proportion of IEDs are traveling waves according to multiple metrics that colocalize to the SOZ, offering hope that IED detection, with further refinement, could serve as an alternative method for SOZ localization.
Databáze:	MEDLINE
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