Search bioRxiv⌕ Search

Biology subjects

Hug, F.

Publications and source records attributed to Hug, F..

21 records · Page 2Linked to original sources

Functional connectivity networks of common synaptic inputs to motor neurons reveal neural spinal synergies during a multi-joint task

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 has been the muscle. In this human study, we decoded the spiking activities of spinal motor neurons innervating six lower limb muscles during an isometric multi-joint task. We identified their common low-frequency components, from which networks of common synaptic inputs to the motor neurons were derived. 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. Our results provide evidence of a synergistic control of a multi-joint motor task at the spinal motor-neuron level. TeaserThe generation of movement involves the activation of many spinal motor neurons from multiple muscles. A central and unresolved question is how these motor neurons are controlled to allow flexibility for adaptation to various mechanical constraints. Since the computational load of controlling each motor neuron independently would be extremely large, the central nervous system presumably adopts dimensionality reduction. We identified networks of functional connectivity between spinal motor neurons based on the common synaptic inputs they receive during a multi-joint task. Our findings revealed functional groupings of motor neurons in a low dimensional space. These groups did not necessarily overlap with the muscle anatomy. We provide a new neural framework for a deeper understanding of movement control in health and disease.

neuroscience↗

Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators

There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 {+/-} 5 motor units were retained (range across operators: 119-134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons x 11 contractions/muscles x 4-28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.

neuroscience↗

Revealing the unique features of each individual her/his muscle activation signatures

There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear Support Vector Machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision making by the machine learning classification model, a Layer-wise Relevance Propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualising each individuals muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.

neuroscience↗