Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, MA 02215, USA; Department of Engineering, University of Palermo, 90128 Palermo, Italy.
Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, MA 02215, USA.
Hum Mov Sci. 2022 Aug;84:102971. doi: 10.1016/j.humov.2022.102971. Epub 2022 Jun 17.
The brain plays a central role in facilitating vital body functions and in regulating physiological and organ systems, including the skeleto-muscular and locomotor system. While neural control is essential to synchronize and coordinate activation of various muscle groups and muscle fibers within muscle groups in relation to body movements and distinct physiologic states, the dynamic networks of brain-muscle interactions have not been explored and the complex regulatory mechanism of brain-muscle control remains unknown. Here we present a first study of network interactions between brain waves at different cortical locations and peripheral muscle activity across key physiologic states - wake, sleep and distinct sleep stages. Utilizing a novel approach based on the Network Physiology framework and the concept of time delay stability, we find that for each physiologic state the network of cortico-muscular interactions is characterized by a specific hierarchical organization of network topology and network links strength, where particular brain waves are main mediators of interaction and control of muscular activity. Further, we uncover that with transition from one physiological state to another, the brain-muscle interaction network undergoes marked reorganization in the profile of network links strength, indicating a direct association between network structure and physiological state and function. The pronounced stratification in brain-muscle network characteristics across sleep stages is consistent for chin and leg muscle groups and persists across subjects, indicating a remarkable universality and a previously unrecognized basic physiologic mechanism that regulates muscle activity even during rest and in the absence targeted direct movement. Our findings demonstrate previously unrecognized coordination between brain waves and activation of different muscle fiber types within muscle groups, laws of brain-muscle cross-communication and principles of network integration and control. These investigations demonstrate the potential of network-based biomarkers for classification of distinct physiological states and conditions, for the diagnosis and prognosis of neurodegenerative, movement and sleep disorders, and for developing efficient treatment strategies.
大脑在促进重要的身体功能和调节生理及器官系统（包括骨骼肌肉和运动系统）方面发挥着核心作用。虽然神经控制对于协调和协调与身体运动和不同生理状态相关的各种肌肉群和肌肉纤维的激活是必不可少的，但大脑-肌肉相互作用的动态网络尚未被探索，大脑-肌肉控制的复杂调节机制仍不清楚。在这里，我们首次研究了不同皮质位置的脑电波与关键生理状态（清醒、睡眠和不同睡眠阶段）下的外周肌肉活动之间的网络相互作用。利用基于网络生理学框架和时滞稳定性概念的新方法，我们发现，对于每种生理状态，皮质-肌肉相互作用的网络都具有特定的网络拓扑和网络链路强度的分层组织，其中特定的脑电波是相互作用和肌肉活动控制的主要介导者。此外，我们发现，随着从一种生理状态向另一种生理状态的转变，大脑-肌肉相互作用网络在网络链路强度的分布上经历了显著的重组，这表明网络结构与生理状态和功能之间存在直接的关联。在睡眠阶段，下巴和腿部肌肉群的大脑-肌肉网络特征明显分层，且在不同的研究对象中都存在，这表明了一种显著的普遍性和一种以前未被认识到的基本生理机制，即使在休息和没有直接靶向运动的情况下，它也可以调节肌肉活动。我们的研究结果表明，大脑电波与肌肉群内不同肌纤维类型的激活之间存在以前未被认识到的协调性、大脑-肌肉交叉通讯的规律以及网络整合和控制的原则。这些研究结果证明了基于网络的生物标志物在区分不同生理状态和条件、诊断和预测神经退行性、运动和睡眠障碍以及开发有效治疗策略方面的潜力。
