Institute of Cognitive Neuroscience, National Central University, Taoyuan 32010, Taiwan.
Institute of Education, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan; Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, United States.
Brain Res. 2022 Dec 1;1796:148075. doi: 10.1016/j.brainres.2022.148075. Epub 2022 Sep 7.
To answer the question of whether the same brain circuit(s) facilitates motor imagery (MI), motor execution (ME), and movement observation (MO), we conducted electroencephalography (EEG) experiment combining the three motor conditions in the same experimental runs. The EEG data were analyzed using two different independent component analysis (ICA) decomposition approaches: a single ICA decomposition on all EEG data combined and separate ICA decomposition on the EEG data obtained from the separate conditions. The results indicated that the separate ICA approach may provide a better fit to the EEG data obtained from the separate conditions to deliver specific independent right mu components with distinct topographies for each of the motor conditions. The topography of the MI condition covered the brain regions posterior to the central sulcus (P4 EEG channel); the ME condition covered the brain regions anterior to the central sulcus (C4 EEG channel), and the MO condition had broader coverage with the main activation in the premotor region (CP4 EEG channel). The source localization results also exhibited significant differences among the motor conditions. In addition, the result of single ICA decomposition resembled the result of separate ICA decomposition on the EEG data of ME with similar topographies and closely located EEG sources. This finding may further indicate that the result of single ICA decomposition may be dominated by the ME motor condition because it manifests higher data variance than the other two motor conditions.
为了回答相同的大脑回路是否有助于运动想象(MI)、运动执行(ME)和运动观察(MO)的问题,我们在同一个实验运行中结合了这三种运动条件进行了脑电图(EEG)实验。使用两种不同的独立成分分析(ICA)分解方法对 EEG 数据进行了分析:对所有 EEG 数据进行单一 ICA 分解,以及对来自单独条件的 EEG 数据进行单独的 ICA 分解。结果表明,单独的 ICA 方法可能更适合于从单独条件获得的 EEG 数据,从而为每个运动条件提供具有独特拓扑的特定独立右 mu 成分。MI 条件的地形图覆盖了中央沟后部的脑区(P4 EEG 通道);ME 条件覆盖了中央沟前部的脑区(C4 EEG 通道),MO 条件的覆盖范围更广,主要激活区在运动前区(CP4 EEG 通道)。源定位结果也表明运动条件之间存在显著差异。此外,单一 ICA 分解的结果与 ME 运动条件的单独 ICA 分解的结果相似,具有相似的拓扑和紧密相邻的 EEG 源。这一发现可能进一步表明,单一 ICA 分解的结果可能主要由 ME 运动条件主导,因为它比其他两种运动条件表现出更高的数据方差。