Kim Yun Kwan, Park Eunhee, Lee Ahee, Im Chang-Hwan, Kim Yun-Hee
Sungkyunkwan University School of Cognitive Science, Seoul, Republic of Korea.
Department of Physical and Rehabilitation Medicine, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.
PLoS One. 2018 Jan 11;13(1):e0190715. doi: 10.1371/journal.pone.0190715. eCollection 2018.
Recent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data.
Twenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions.
Motor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC.
These findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.
近期有关大脑功能或有效连接性的研究报告称，在运动执行和运动想象过程中，与运动相关的脑区会被激活，但运动与认知区域之间的关系尚未完全明晰。我们研究的目的是分析运动与认知网络之间的有效连接性，以确定健康个体在运动执行和运动想象过程中的网络动态。其次，我们使用脑电图（EEG）数据的动态因果模型（DCM）分析了运动执行和想象过程中正确与错误反应之间有效连接性的差异。
20名健康受试者进行了一系列手指敲击试验，采用运动执行或运动想象方式，并记录其表现。使用动态因果模型估计初级运动皮层（M1）、辅助运动区（SMA）、运动前区皮层（PMC）和背外侧前额叶皮层（DLPFC）之间有效连接性的变化。应用具有家族水平推断和固定效应分析的贝叶斯模型平均法来确定这些区域最可能的连接模型。
运动执行和想象分别显示出对不同脑区的输入，即运动前区皮层和辅助运动区。在运动执行过程中，从DLPFC到PMC的前馈网络的耦合强度大于运动想象过程中的耦合强度。在运动想象过程中，从PMC到SMA的前馈网络以及从M1到PMC的反馈网络的耦合强度高于运动执行过程中的耦合强度。在想象运动中，尽管正确与错误任务反应之间存在连接性差异，但每个包含正确和错误反应的运动想象任务都显示出相似的网络连接特征。正确的运动想象反应显示出从PMC到DLPFC的连接，而错误反应则具有从SMA到DLPFC的特征性连接。
这些发现有助于理解运动执行和想象过程中运动与认知区域之间的有效连接性，也为未来对中风患者进行连接性研究奠定了基础。
