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