Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, People's Republic of China.
National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, People's Republic of China.
J Neural Eng. 2021 May 13;18(4). doi: 10.1088/1741-2552/abd685.
. Complex biological systems consist of multi-level mechanism in terms of within- and cross-subsystems correlations, and they are primarily manifested in terms of connectivity, multiscale properties, and nonlinearity. Existing studies have each only explored one aspect of the functional corticocortical coupling (FCCC), which has some limitations in portraying the complexity of multivariable systems. The present study investigated the direct interactions of brain networks at multiple time scales.. We extended the multivariate transfer entropy (MuTE) method and proposed a novel method, named multiscale multivariate transfer entropy (MSMVTE), to explore the direct interactions of brain networks across multiple time scale. To verify this aim, we introduced three simulation models and compared them with multiscale transfer entropy (MSTE) and MuTE methods. We then applied MSMVTE method to analyze FCCC during a unilateral right-hand steady-state force task.. Simulation results showed that the MSMVTE method, compared with MSTE and MuTE methods, better detected direct interactions and avoid the spurious effects of indirect relationships. Further analysis of experimental data showed that the connectivity from left premotor/sensorimotor cortex to right premotor/sensorimotor cortex was significantly higher than that of opposite directionality. Furthermore, the connectivities from central motor areas to both sides of premotor/sensorimotor areas were higher than those of opposite directionalities. Additionally, the maximum coupling strength was found to occur at a specific scale (3-10).. Simulation results confirmed the effectiveness of the MSMVTE method to describe direct relationships and multiscale characteristics in complex systems. The enhancement of FCCC reflects the interaction of more extended activation in cortical motor regions. Additionally, the neurodynamic process of brain depends not only on emergent behavior at small scales, but also on the constraining effects of the activity at large scales. Taken together, our findings provide a basis for better understanding dynamics in brain networks.
. 复杂的生物系统由多层次的机制组成，包括子系统内和子系统间的相关性，这些系统主要表现为连接性、多尺度特性和非线性。现有的研究仅探索了功能皮质间耦合 (FCCC) 的一个方面，这在描述多变量系统的复杂性方面存在一些局限性。本研究探讨了多个时间尺度上的脑网络的直接相互作用。我们扩展了多变量传递熵 (MuTE) 方法，并提出了一种新的方法，称为多尺度多变量传递熵 (MSMVTE)，以探索跨多个时间尺度的脑网络的直接相互作用。为了验证这一目标，我们引入了三个模拟模型，并将其与多尺度传递熵 (MSTE) 和 MuTE 方法进行了比较。然后，我们应用 MSMVTE 方法分析单侧右手稳态力任务期间的 FCCC。模拟结果表明，与 MSTE 和 MuTE 方法相比，MSMVTE 方法更好地检测了直接相互作用，并避免了间接关系的虚假效应。对实验数据的进一步分析表明，从左侧运动前/感觉运动皮层到右侧运动前/感觉运动皮层的连接性明显高于相反方向的连接性。此外，从中枢运动区到双侧运动前/感觉运动区的连接性高于相反方向的连接性。此外，最大耦合强度出现在特定尺度 (3-10) 上。模拟结果证实了 MSMVTE 方法在描述复杂系统中的直接关系和多尺度特征的有效性。FCCC 的增强反映了皮质运动区中更广泛激活的相互作用。此外，大脑的神经动力学过程不仅取决于小尺度上的突发行为，还取决于大尺度上的活动的约束效应。综上所述，我们的研究结果为更好地理解大脑网络的动力学提供了基础。
