Ebrahimzadeh Elias, Shams Mohammad, Seraji Masoud, Sadjadi Seyyed Mostafa, Rajabion Lila, Soltanian-Zadeh Hamid
CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
Front Neurol. 2021 Nov 15;12:695997. doi: 10.3389/fneur.2021.695997. eCollection 2021.
Conventional EEG-fMRI methods have been proven to be of limited use in the sense that they cannot reveal the information existing in between the spikes. To resolve this issue, the current study obtains the epileptic components time series detected on EEG and uses them to fit the Generalized Linear Model (GLM), as a substitution for classical regressors. This approach allows for a more precise localization, and equally importantly, the prediction of the future behavior of the epileptic generators. The proposed method approaches the localization process in the component domain, rather than the electrode domain (EEG), and localizes the generators through investigating the spatial correlation between the candidate components and the spike template, as well as the medical records of the patient. To evaluate the contribution of EEG-fMRI and concordance between fMRI and EEG, this method was applied on the data of 30 patients with refractory epilepsy. The results demonstrated the significant numbers of 29 and 24 for concordance and contribution, respectively, which mark improvement as compared to the existing literature. This study also shows that while conventional methods often fail to properly localize the epileptogenic zones in deep brain structures, the proposed method can be of particular use. For further evaluation, the concordance level between IED-related BOLD clusters and Seizure Onset Zone (SOZ) has been quantitatively investigated by measuring the distance between IED/SOZ locations and the BOLD clusters in all patients. The results showed the superiority of the proposed method in delineating the spike-generating network compared to conventional EEG-fMRI approaches. In all, the proposed method goes beyond the conventional methods by breaking the dependency on spikes and using the outside-the-scanner spike templates and the selected components, achieving an accuracy of 97%. Doing so, this method contributes to improving the yield of EEG-fMRI and creates a more realistic perception of the neural behavior of epileptic generators which is almost without precedent in the literature.
传统的脑电图-功能磁共振成像（EEG-fMRI）方法已被证明用途有限，因为它们无法揭示尖峰之间存在的信息。为了解决这个问题，当前的研究获取了在脑电图上检测到的癫痫成分时间序列，并使用它们来拟合广义线性模型（GLM），以替代经典回归变量。这种方法能够实现更精确的定位，同样重要的是，能够预测癫痫发生器的未来行为。所提出的方法在成分域而非电极域（脑电图）中进行定位过程，并通过研究候选成分与尖峰模板之间的空间相关性以及患者的病历记录来定位发生器。为了评估脑电图-功能磁共振成像的贡献以及功能磁共振成像与脑电图之间的一致性，该方法应用于30例难治性癫痫患者的数据。结果显示，一致性和贡献的显著数字分别为29和24，与现有文献相比有了改进。这项研究还表明，虽然传统方法常常无法正确定位深部脑结构中的致痫区，但所提出的方法可能会有特别的用处。为了进一步评估，通过测量所有患者中发作间期癫痫样放电（IED）相关的血氧水平依赖（BOLD）簇与癫痫发作起始区（SOZ）之间的距离，对IED相关的BOLD簇与SOZ之间的一致性水平进行了定量研究。结果表明，与传统的脑电图-功能磁共振成像方法相比，所提出的方法在描绘尖峰生成网络方面具有优越性。总体而言，所提出的方法通过打破对尖峰的依赖，使用扫描仪外的尖峰模板和选定的成分，超越了传统方法，实现了97%的准确率。这样做，该方法有助于提高脑电图-功能磁共振成像的成功率，并对癫痫发生器的神经行为产生更现实的认识，这在文献中几乎是前所未有的。
