Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
Neuroimage. 2022 Jul 1;254:119132. doi: 10.1016/j.neuroimage.2022.119132. Epub 2022 Mar 23.
Determining the accurate locations of interictal spikes has been fundamental in the presurgical evaluation of epilepsy surgery. Stereo-electroencephalography (SEEG) is able to directly record cortical activity and localize interictal spikes. However, the main caveat of SEEG techniques is that they have limited spatial sampling (covering <5% of the whole brain), which may lead to missed spikes originating from brain regions that were not covered by SEEG. To address this problem, we propose a SEEG-informed minimum-norm estimates (SIMNE) method by combining SEEG with magnetoencephalography (MEG) or EEG. Specifically, the spike locations determined by SEEG offer as a priori information to guide MEG source reconstruction. Both computer simulations and experiments using data from five epilepsy patients were conducted to evaluate the performance of SIMNE. Our results demonstrate that SIMNE generates more accurate source estimation than a traditional minimum-norm estimates method and reveals the locations of spikes missed by SEEG, which would improve presurgical evaluation of the epileptogenic zone.
确定发作间期棘波的准确位置一直是癫痫手术术前评估的基础。立体脑电图（SEEG）能够直接记录皮质活动并定位发作间期棘波。然而，SEEG 技术的主要缺点是空间采样有限（<5%的大脑），这可能导致源自未被 SEEG 覆盖的脑区的棘波漏检。为了解决这个问题，我们提出了一种通过将 SEEG 与脑磁图（MEG）或脑电图（EEG）相结合的 SEEG 信息最小范数估计（SIMNE）方法。具体来说，SEEG 确定的棘波位置作为先验信息来指导 MEG 源重建。我们通过对五名癫痫患者的数据进行计算机模拟和实验，来评估 SIMNE 的性能。结果表明，SIMNE 生成的源估计比传统的最小范数估计方法更准确，并揭示了 SEEG 漏检的棘波位置，这将有助于提高致痫灶的术前评估。
