PDF(Pubmed)
Abstract：
Electro/Magneto-EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth-weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high-density EEG (HD-EEG) simulations of epileptic activity and actual MEG/HD-EEG recordings from patients with focal epilepsy. We incorporated depth-weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD-EEG involving a wide range of spatial extents and signal-to-noise ratio (SNR) levels, before investigating EMSI on clinical HD-EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth-weighted cMEM and depth-weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth-weighted cMEM improved the localization when compared to cMEM and depth-weighted MNE, whereas depth-weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients\' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD-EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth-weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD-EEG and clinical MEG and HD-EEG for epilepsy patients.
摘要：
由于EEG/MEG对浅表区域和皮质下结构的空间配置的更高灵敏度，来自深层发生器的癫痫活动的电/磁脑图（EEG/MEG）源成像（EMSI）通常具有挑战性。我们先前证明了均值上的相干最大熵（cMEM）方法精确定位浅层皮层发生器及其空间范围的能力。这里,我们提出了一种深度加权自适应的cMEM，以更准确地定位深度生成器。使用癫痫活动的真实MEG/高密度EEG（HD-EEG）模拟和局灶性癫痫患者的实际MEG/HD-EEG记录来评估这些方法。我们在MEM框架中加入了深度加权，以补偿其对表面生成器的偏好。我们还包括了两个海马的网格，作为源模型中的附加深层结构。我们为MEG和HD-EEG生成了5400次发作间癫痫放电的真实模拟，涉及广泛的空间范围和信噪比（SNR）水平，在研究EMSI对16例患者的临床HD-EEG和14例患者的MEG之前。通过目视检查标记临床发作间癫痫放电。我们应用了三种EMSI方法：cMEM，深度加权cMEM和深度加权最小范数估计(MNE)。地面实况被定义为真实的模拟发生器或基于患者可用的临床信息的绘制区域。对于深层来源，与cMEM和深度加权MNE相比，深度加权cMEM改进了定位，而深度加权cMEM不会降低浅表区域的定位精度。对于患者数据，我们观察到深度源的本地化有所改善，尤其是内侧颞叶癫痫患者，cMEM未能重建海马中的初始发生器。深度加权对于MEG（梯度计）比HD-EEG更为重要。当考虑MEM的小波扩展的深度加权时，发现了类似的发现。总之,深度加权cMEM改善了深层源的定位，而不会或最小程度地降低了浅层源的定位。对于癫痫患者，使用MEG和HD-EEG以及临床MEG和HD-EEG进行的广泛模拟证明了这一点。
