Strobbe Gregor, Carrette Evelien, López José David, Montes Restrepo Victoria, Van Roost Dirk, Meurs Alfred, Vonck Kristl, Boon Paul, Vandenberghe Stefaan, van Mierlo Pieter
Ghent University, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000 Ghent, Belgium; iMinds Medical IT Department, Belgium.
Laboratory for Clinical and Experimental Neurophysiology, Ghent University Hospital, Ghent, Belgium.
Neuroimage Clin. 2016 Jan 20;11:252-263. doi: 10.1016/j.nicl.2016.01.017. eCollection 2016.
Electrical source imaging of interictal spikes observed in EEG recordings of patients with refractory epilepsy provides useful information to localize the epileptogenic focus during the presurgical evaluation. However, the selection of the time points or time epochs of the spikes in order to estimate the origin of the activity remains a challenge. In this study, we consider a Bayesian EEG source imaging technique for distributed sources, i.e. the multiple volumetric sparse priors (MSVP) approach. The approach allows to estimate the time courses of the intensity of the sources corresponding with a specific time epoch of the spike. Based on presurgical averaged interictal spikes in six patients who were successfully treated with surgery, we estimated the time courses of the source intensities for three different time epochs: (i) an epoch starting 50 ms before the spike peak and ending at 50% of the spike peak during the rising phase of the spike, (ii) an epoch starting 50 ms before the spike peak and ending at the spike peak and (iii) an epoch containing the full spike time period starting 50 ms before the spike peak and ending 230 ms after the spike peak. To identify the primary source of the spike activity, the source with the maximum energy from 50 ms before the spike peak till 50% of the spike peak was subsequently selected for each of the time windows. For comparison, the activity at the spike peaks and at 50% of the peaks was localized using the LORETA inversion technique and an ECD approach. Both patient-specific spherical forward models and patient-specific 5-layered finite difference models were considered to evaluate the influence of the forward model. Based on the resected zones in each of the patients, extracted from post-operative MR images, we compared the distances to the resection border of the estimated activity. Using the spherical models, the distances to the resection border for the MSVP approach and each of the different time epochs were in the same range as the LORETA and ECD techniques. We found distances smaller than 23 mm, with robust results for all the patients. For the finite difference models, we found that the distances to the resection border for the MSVP inversions of the full spike time epochs were generally smaller compared to the MSVP inversions of the time epochs before the spike peak. The results also suggest that the inversions using the finite difference models resulted in slightly smaller distances to the resection border compared to the spherical models. The results we obtained are promising because the MSVP approach allows to study the network of the estimated source-intensities and allows to characterize the spatial extent of the underlying sources.
在难治性癫痫患者的脑电图记录中观察到的发作间期棘波的电源成像,为术前评估期间定位致痫灶提供了有用信息。然而,为了估计活动的起源而选择棘波的时间点或时间段仍然是一个挑战。在本研究中,我们考虑一种用于分布式源的贝叶斯脑电图源成像技术,即多体积稀疏先验(MSVP)方法。该方法允许估计与棘波的特定时间段相对应的源强度的时间进程。基于6例成功接受手术治疗的患者的术前平均发作间期棘波,我们估计了三个不同时间段的源强度的时间进程:(i)一个时间段,从棘波峰值前50毫秒开始,在棘波上升阶段结束于棘波峰值的50%,(ii)一个时间段,从棘波峰值前50毫秒开始,结束于棘波峰值,以及(iii)一个包含整个棘波时间段的时间段,从棘波峰值前50毫秒开始,结束于棘波峰值后230毫秒。为了识别棘波活动的主要来源,随后为每个时间窗口选择从棘波峰值前50毫秒到棘波峰值的50%能量最大的源。为了进行比较,使用LORETA反演技术和ECD方法对棘波峰值和峰值的50%处的活动进行定位。考虑了患者特异性球形正向模型和患者特异性5层有限差分模型来评估正向模型的影响。基于从术后磁共振图像中提取的每个患者的切除区域,我们比较了估计活动到切除边界的距离。使用球形模型,MSVP方法和每个不同时间段到切除边界的距离与LORETA和ECD技术在相同范围内。我们发现距离小于23毫米,所有患者的结果都很可靠。对于有限差分模型,我们发现与棘波峰值前时间段的MSVP反演相比,整个棘波时间段的MSVP反演到切除边界的距离通常更小。结果还表明,与球形模型相比,使用有限差分模型的反演到切除边界的距离略小。我们获得的结果很有前景,因为MSVP方法允许研究估计的源强度网络,并允许表征潜在源的空间范围。