LAMHESS, Université Côte d'Azur, Nice, France.
Laboratory 'Movement, Interactions, Performance' (EA 4334), Nantes University, Nantes, France.
J Physiol. 2023 Aug;601(15):3201-3219. doi: 10.1113/JP283040. Epub 2022 Jul 13.
Movements are reportedly controlled through the combination of synergies that generate specific motor outputs by imposing an activation pattern on a group of muscles. To date, the smallest unit of analysis of these synergies has been the muscle through the measurement of its activation. However, the muscle is not the lowest neural level of movement control. In this human study (n = 10), we used a purely data-driven method grounded on graph theory to extract networks of motor neurons based on their correlated activity during an isometric multi-joint task. Specifically, high-density surface electromyography recordings from six lower limb muscles were decomposed into motor neurons spiking activity. We analysed these activities by identifying their common low-frequency components, from which networks of correlated activity to the motor neurons were derived and interpreted as networks of common synaptic inputs. The vast majority of the identified motor neurons shared common inputs with other motor neuron(s). In addition, groups of motor neurons were partly decoupled from their innervated muscle, such that motor neurons innervating the same muscle did not necessarily receive common inputs. Conversely, some motor neurons from different muscles-including distant muscles-received common inputs. The study supports the theory that movements are produced through the control of small numbers of groups of motor neurons via common inputs and that there is a partial mismatch between these groups of motor neurons and muscle anatomy. We provide a new neural framework for a deeper understanding of the structure of common inputs to motor neurons. KEY POINTS: A central and unresolved question is how spinal motor neurons are controlled to generate movement. We decoded the spiking activities of dozens of spinal motor neurons innervating six muscles during a multi-joint task, and we used a purely data-driven method grounded on graph theory to extract networks of motor neurons based on their correlated activity (considered as common input). The vast majority of the identified motor neurons shared common inputs with other motor neuron(s). Groups of motor neurons were partly decoupled from their innervated muscle, such that motor neurons innervating the same muscle did not necessarily receive common inputs. Conversely, some motor neurons from different muscles, including distant muscles, received common inputs. The study supports the theory that movement is produced through the control of groups of motor neurons via common inputs and that there is a partial mismatch between these groups of motor neurons and muscle anatomy.
据报道，运动是通过协同作用来控制的，这些协同作用通过在一组肌肉上施加激活模式来产生特定的运动输出。迄今为止，这些协同作用的最小分析单元是肌肉，通过测量其激活来实现。然而，肌肉并不是运动控制的最低神经水平。在这项人体研究（n=10）中，我们使用了一种纯粹基于数据驱动的方法，该方法基于图论，从在等长多关节任务期间相关活动的角度提取运动神经元网络。具体来说，来自六个下肢肌肉的高密度表面肌电图记录被分解为运动神经元的尖峰活动。我们通过识别它们共同的低频成分来分析这些活动，从这些共同的活动中得出了运动神经元的相关网络，并将其解释为共同突触输入的网络。绝大多数识别出的运动神经元与其他运动神经元共享共同的输入。此外，运动神经元组与它们所支配的肌肉部分分离，因此支配同一肌肉的运动神经元不一定接收共同的输入。相反，一些来自不同肌肉的运动神经元——包括远距离肌肉——接收共同的输入。这项研究支持了这样一种理论，即运动是通过控制少数运动神经元组来产生的，这些运动神经元组通过共同的输入进行控制，并且这些运动神经元组与肌肉解剖结构之间存在部分不匹配。我们为深入了解运动神经元共同输入的结构提供了一个新的神经框架。关键点：一个核心且未解决的问题是，脊髓运动神经元如何受到控制以产生运动。我们解码了在多关节任务期间支配六个肌肉的数十个脊髓运动神经元的尖峰活动，并且我们使用了一种纯粹基于数据驱动的方法，该方法基于图论，从相关活动（被认为是共同输入）的角度提取运动神经元网络。绝大多数识别出的运动神经元与其他运动神经元共享共同的输入。运动神经元组与它们所支配的肌肉部分分离，因此支配同一肌肉的运动神经元不一定接收共同的输入。相反，一些来自不同肌肉的运动神经元，包括远距离肌肉，接收共同的输入。该研究支持这样一种理论，即运动是通过控制运动神经元组通过共同输入产生的，并且这些运动神经元组与肌肉解剖结构之间存在部分不匹配。
