He Borong, Salmas Paola, Zhang Jing, Duan Xiaojie, Cheung Vincent C K
School of Biomedical Sciences, Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China.
The James Franck Institute, The University of Chicago, Chicago, Illinois, USA.
J Physiol. 2025 Aug;603(16):4659-4679. doi: 10.1113/JP288073. Epub 2025 Jul 28.
The central nervous system controls movement by combining neuromotor modules, known as muscle synergies. Previous studies suggest that spinal premotor interneurons (PreM-INs) contribute to the encoding of stable muscle synergies for voluntary movement. But descending and sensory inputs also influence motor outputs through the spinal interneuronal network, which may be configured by its inputs to encode different sets of muscle synergies depending on the network state, thereby recruiting different selections of synergies. Here we tested this possibility of state-dependent synergy encoding by examining the muscle synergies represented by the same upstream spinal interneurons under different activity states induced by various optogenetic stimulation patterns. Lumbosacral spinal units and electromyographic (EMG) activities of hindlimb muscles were simultaneously recorded from anaesthetized Thy1-ChR2 mice as the spinal cord was stimulated by one or two optic fibres at different intensities. The synergy encoded by each unit was revealed as a 'muscle field' derived from spike-triggered averages of EMG, whereas the entire muscle synergy set was factorized from the EMG. We found that although the muscle synergy set remained stable across stimulation conditions, the muscle fields of the same units were matched to different synergies within the set in different states. Thus the interneurons may flexibly adjust their connectivity with the motoneurons of the muscles as descending and sensory afferents impose different states on the spinal network. State-dependent encoding of muscle synergies may allow different synergies to be selected for producing stable movement in an ever-changing workspace environment. KEY POINTS: Muscle synergies for locomotion can be represented by spinal interneurons, as revealed by the interneurons' muscle fields derived from spike-triggered averages of EMG. The muscle field of a single spinal interneuron may vary under different stimulation conditions, as demonstrated by optogenetic stimulation. Encoding of muscle synergies is dependent on the state of spinal activities, thus facilitating the selection of appropriate synergies in different dynamic environments.
中枢神经系统通过整合被称为肌肉协同作用的神经运动模块来控制运动。先前的研究表明,脊髓运动前中间神经元(PreM-INs)有助于对自愿运动的稳定肌肉协同作用进行编码。但是下行和感觉输入也通过脊髓中间神经元网络影响运动输出,该网络可能根据其输入进行配置,以根据网络状态编码不同的肌肉协同作用集,从而募集不同的协同作用选择。在这里,我们通过检查由各种光遗传学刺激模式诱导的不同活动状态下相同上游脊髓中间神经元所代表的肌肉协同作用,来测试这种状态依赖性协同作用编码的可能性。在麻醉的Thy1-ChR2小鼠中,当用一根或两根光导纤维以不同强度刺激脊髓时,同时记录腰荐脊髓单位和后肢肌肉的肌电图(EMG)活动。每个单位编码的协同作用表现为从EMG的触发平均脉冲中得出的“肌肉场”,而整个肌肉协同作用集则从EMG中分解出来。我们发现,尽管肌肉协同作用集在不同刺激条件下保持稳定,但同一单位的肌肉场在不同状态下与该集合中的不同协同作用相匹配。因此,随着下行和感觉传入在脊髓网络上施加不同状态,中间神经元可能会灵活地调整它们与肌肉运动神经元的连接。肌肉协同作用的状态依赖性编码可能允许选择不同的协同作用,以便在不断变化的工作空间环境中产生稳定的运动。要点:运动的肌肉协同作用可以由脊髓中间神经元来表示,这是由从EMG的触发平均脉冲中得出的中间神经元的肌肉场所揭示的。如光遗传学刺激所示,单个脊髓中间神经元的肌肉场在不同刺激条件下可能会有所不同。肌肉协同作用的编码取决于脊髓活动的状态,从而有助于在不同的动态环境中选择合适的协同作用。