Correlation networks of spinal motor neurons that innervate lower limb muscles during a multi-joint isometric task
File(s)
Published version
Author(s)
Hug, François
Avrillon, Simon
Sarcher, Aurélie
Del Vecchio, Alessandro
Farina, Dario
Type
Journal Article
Abstract
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 analyzed 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. Our 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.
Abstract figure legend Ten participants performed an isometric multi-joint task, which consisted in producing force on an instrumented pedal. Adhesive grids of 64 electrodes were placed over six lower limb muscles (gastrocnemius medialis [GM] and lateralis [GL], vastus lateralis [VL] and medialis [VM], biceps femoris [BF], semitendinosus [ST]). The high-density EMG signals were decomposed into motor unit spike trains. For each pair of motor neurons, we assessed the correlation between their smoothed discharge rates to determine whether they shared common input. Then, we used a purely data-driven method grounded on graph theory to extract networks of common inputs and we applied a clustering procedure to group the motor neurons according to their positions in the graph (i.e., their correlated activity). Results support the theory that movement is 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.
Abstract figure legend Ten participants performed an isometric multi-joint task, which consisted in producing force on an instrumented pedal. Adhesive grids of 64 electrodes were placed over six lower limb muscles (gastrocnemius medialis [GM] and lateralis [GL], vastus lateralis [VL] and medialis [VM], biceps femoris [BF], semitendinosus [ST]). The high-density EMG signals were decomposed into motor unit spike trains. For each pair of motor neurons, we assessed the correlation between their smoothed discharge rates to determine whether they shared common input. Then, we used a purely data-driven method grounded on graph theory to extract networks of common inputs and we applied a clustering procedure to group the motor neurons according to their positions in the graph (i.e., their correlated activity). Results support the theory that movement is 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.
Date Issued
2023-08-01
Date Acceptance
2022-06-22
Citation
The Journal of Physiology, 2023, 601 (15), pp.3201-3219
ISSN
0022-3751
Publisher
Wiley
Start Page
3201
End Page
3219
Journal / Book Title
The Journal of Physiology
Volume
601
Issue
15
Copyright Statement
© 2022 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Publication Status
Published
Date Publish Online
2022-06-30