The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, China.
Faculty of natural science, University of Stirling, Stirling, United Kingdom.
Neuroimage Clin. 2019;22:101759. doi: 10.1016/j.nicl.2019.101759. Epub 2019 Mar 12.
Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME.
癫痫的特征是神经元活动的超同步爆发，发作可以以不同的方式传播到任何和所有区域，导致大脑网络动态组织。然而，癫痫患者头皮 EEG 的网络特征与血氧水平依赖（BOLD）反应之间的关系尚不清楚。在这项研究中，同时采集了 18 名青少年肌阵挛性癫痫（JME）患者的 EEG 和 fMRI 数据。然后，计算 EEG 电极之间的自适应定向传递函数（ADTF）值来定义时变网络。在 fMRI 分析中，使用滑动窗口内网络信息流的变化作为时间回归器来预测 BOLD 反应。为了研究 EEG 依赖的功能耦合，对头皮 EEG 和 BOLD 时程的网络变化进行了调制相互作用分析。结果表明，与高网络变化相关的 BOLD 激活主要位于丘脑、小脑、顶叶下小叶和感觉运动相关区域，包括中央扣带回（MCC）、辅助运动区（SMA）和旁中央小叶。与中等网络变化相关的 BOLD 失活出现在额、顶和枕叶区域。此外，调制相互作用分析表明，丘脑、小脑、额叶和感觉运动相关区域之间存在主要的负向调制作用。本研究描述了一种将 BOLD 反应与头皮 EEG 的同时功能网络组织联系起来的新方法。这些发现表明，使用电极之间的功能连接变化预测癫痫活动是有效的。丘脑、额叶区域、小脑和感觉运动相关区域之间的功能耦合可能与癫痫的发生和传播有关，这为 JME 的病理生理机制和干预靶点提供了新的见解。
