Non-invasive estimation of synaptic inputs to human spinal α-motoneurons : effects of joint position and central nervous system injury

Overview & Synopsis

Despite the complexity of the neural networks underlying voluntary movement, muscle contraction ultimately relies on a single type of neuron, the α-motoneuron. All the influences acting on it can be grouped into three main types of synaptic inputs: excitatory, inhibitory and neuromodulatory. These different inputs leave characteristic signatures in the activity of α-motoneurons. Based on this property, it is possible to estimate the synaptic inputs driving the activity of α-motoneurons, which can itself be obtained through decomposition of surface electromyography signals. On this basis, the work of this thesis aims to estimate the synaptic inputs of α-motoneurons during changes in joint position, as well as after stroke or spinal cord injury (SCI). This work has highlighted an increase in inhibitory inputs after stroke or SCI, suggesting that they contribute to the reduced ability to increase α-motoneuron activity. Furthermore, whereas healthy participants appear to adapt their synaptic inputs to joint position, notably via regulation of inhibitory inputs, such adaptation is not observed after stroke or SCI. These results indicate that inhibitory inputs are both excessive and poorly adaptable in these two pathologies. Finally, an increase in neuromodulatory inputs was observed only after stroke, suggesting a distinct organisation of synaptic inputs contributing to muscle weakness, and potentially calling for differentiated therapeutic strategies.