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一种空间扰动框架,用于验证致痫区的植入。

A spatial perturbation framework to validate implantation of the epileptogenic zone.

机构信息

Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada.

Department of Biomedical Engineering, Duke Pratt School of Engineering, Durham, NC, USA.

出版信息

Nat Commun. 2024 Jun 19;15(1):5253. doi: 10.1038/s41467-024-49470-z.

Abstract

Stereo-electroencephalography (SEEG) is the gold standard to delineate surgical targets in focal drug-resistant epilepsy. SEEG uses electrodes placed directly into the brain to identify the seizure-onset zone (SOZ). However, its major constraint is limited brain coverage, potentially leading to misidentification of the 'true' SOZ. Here, we propose a framework to assess adequate SEEG sampling by coupling epileptic biomarkers with their spatial distribution and measuring the system's response to a perturbation of this coupling. We demonstrate that the system's response is strongest in well-sampled patients when virtually removing the measured SOZ. We then introduce the spatial perturbation map, a tool that enables qualitative assessment of the implantation coverage. Probability modelling reveals a higher likelihood of well-implanted SOZs in seizure-free patients or non-seizure free patients with incomplete SOZ resections, compared to non-seizure-free patients with complete resections. This highlights the framework's value in sparing patients from unsuccessful surgeries resulting from poor SEEG coverage.

摘要

立体脑电图 (SEEG) 是描绘局灶性耐药性癫痫手术靶区的金标准。SEEG 使用直接放置在大脑中的电极来识别癫痫发作起始区 (SOZ)。然而,其主要限制是大脑覆盖范围有限,可能导致“真正”SOZ 的错误识别。在这里,我们通过将癫痫生物标志物与其空间分布相结合,并测量系统对这种耦合的扰动的响应,提出了一种评估充分 SEEG 采样的框架。我们证明,当虚拟去除测量的 SOZ 时,系统在采样良好的患者中响应最强。然后,我们引入了空间扰动图,这是一种可以定性评估植入物覆盖范围的工具。概率建模显示,与完全切除的非无癫痫发作患者相比,无癫痫发作的患者或无完全切除的非无癫痫发作患者中,SOZ 植入良好的可能性更高。这凸显了该框架在避免因 SEEG 覆盖范围不佳而导致手术失败方面的价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ade4/11187199/70a1c282013c/41467_2024_49470_Fig1_HTML.jpg

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