Gong Jinnan, Luo Cheng, Chang Xuebin, Zhang Rui, Klugah-Brown Benjamin, Guo Lanjin, Xu Peng, Yao Dezhong
Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Brain Topogr. 2017 Nov;30(6):797-809. doi: 10.1007/s10548-017-0581-z. Epub 2017 Aug 7.
The rhythm of electroencephalogram (EEG) depends on the neuroanatomical-based parameters such as white matter (WM) connectivity. However, the impacts of these parameters on the specific characteristics of EEG have not been clearly understood. Previous studies demonstrated that, these parameters contribute the inter-subject differences of EEG during performance of specific task such as motor imagery (MI). Though researchers have worked on this phenomenon, the idea is yet to be understood in terms of the mechanism that underlies such differences. Here, to tackle this issue, we began our investigations by first examining the structural features related to scalp EEG characteristics, which are event-related desynchronizations (ERDs), during MI using diffusion MRI. Twenty-four right-handed subjects were recruited to accomplish MI tasks and MRI scans. Based on the high spatial resolution of the structural and diffusion images, the motor-related WM links, such as basal ganglia (BG)-primary somatosensory cortex (SM1) pathway and supplementary motor area (SMA)-SM1 connection, were reconstructed by using probabilistic white matter tractography. Subsequently, the relationships of WM characteristics with EEG signals were investigated. These analyses demonstrated that WM pathway characteristics, including the connectivity strength and the positional characteristics of WM connectivity on SM1 (defined by the gyrus-sulcus ratio of connectivity, GSR), have a significant impact on ERDs when doing MI. Interestingly, the high GSR of WM connections between SM1 and BG were linked to the better ERDs. These results therefore, indicated that the connectivity in the gyrus of SM1 interacted with MI network which played the critical role for the scalp EEG signal extraction of MI to a great extent. The study provided the coupling mechanism between structural and dynamic physiological features of human brain, which would also contribute to understanding individual differences of EEG in MI-brain computer interface.
脑电图（EEG）的节律取决于基于神经解剖学的参数，如白质（WM）连通性。然而，这些参数对EEG特定特征的影响尚未得到清晰的理解。先前的研究表明，这些参数导致了在执行特定任务（如运动想象，MI）期间EEG的个体间差异。尽管研究人员已对这一现象进行了研究，但从这种差异背后的机制角度来看，这一观点仍有待理解。在此，为解决这一问题，我们首先通过使用扩散磁共振成像（MRI）检查与头皮EEG特征相关的结构特征，即事件相关去同步化（ERD），来开始我们的研究。招募了24名右利手受试者来完成MI任务和MRI扫描。基于结构和扩散图像的高空间分辨率，通过使用概率性白质纤维束成像重建了与运动相关的WM连接，如基底神经节（BG）-初级体感皮层（SM1）通路和辅助运动区（SMA）-SM1连接。随后，研究了WM特征与EEG信号之间的关系。这些分析表明，WM通路特征，包括连通性强度以及WM在SM1上连通性的位置特征（由连通性的脑回-脑沟比率，GSR定义），在进行MI时对ERD有显著影响。有趣的是，SM1和BG之间WM连接的高GSR与更好的ERD相关。因此，这些结果表明，SM1脑回中的连通性与MI网络相互作用，这在很大程度上对MI脑机接口的头皮EEG信号提取起着关键作用。该研究提供了人类大脑结构和动态生理特征之间的耦合机制，这也将有助于理解MI脑机接口中EEG的个体差异。
