Kerkman Jennifer N, Daffertshofer Andreas, Gollo Leonardo L, Breakspear Michael, Boonstra Tjeerd W
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1567-1570. doi: 10.1109/EMBC.2017.8037136.
Physiological networks reveal information about the interaction between subsystems of the human body. Here we investigated the interaction between the central nervous system and the musculoskeletal system by mapping functional muscle networks. Muscle networks were extracted using coherence analysis of muscle activity assessed using surface electromyography (EMG). Surface EMG was acquired from 36 muscles distributed throughout the body while participants were standing upright and performing a bimanual pointing task. Non-negative matrix factorization revealed functional connectivity in four frequency bands. The spatial arrangement differed considerably across frequencies supporting a multiplex network organisation. Graph-theory analysis of layer-specific network revealed a consistent fat-tail distribution of the edges weights, distinct efficiency values, and core-periphery properties. These frequency bands may be spectral fingerprints of different neural pathways that innervate the spinal motor neurons to control the musculoskeletal system.
生理网络揭示了人体子系统之间相互作用的信息。在此,我们通过绘制功能性肌肉网络来研究中枢神经系统与肌肉骨骼系统之间的相互作用。使用表面肌电图(EMG)评估的肌肉活动相干分析来提取肌肉网络。在参与者直立并执行双手指向任务时,从遍布全身的36块肌肉采集表面肌电图。非负矩阵分解揭示了四个频带中的功能连接性。不同频率下的空间排列差异很大,支持多重网络组织。对特定层网络的图论分析揭示了边权重一致的肥尾分布、不同的效率值和核心-外围属性。这些频带可能是支配脊髓运动神经元以控制肌肉骨骼系统的不同神经通路的频谱指纹。