Synergistic Organization of Neural Inputs from Spinal Motor Neurons to Extrinsic and Intrinsic Hand Muscles

Abstract

Our current understanding of synergistic muscle control is based on the analysis of muscle activities. Modules (synergies) in muscle coordination are extracted from electromyographic (EMG) signal envelopes. Each envelope indirectly reflects the neural drive received by a muscle; therefore, it carries information on the overall activity of the innervating motor neurons. However, it is not known whether the output of spinal motor neurons, whose number is orders of magnitude greater than the muscles they innervate, is organized in a low-dimensional fashion when performing complex tasks. Here, we hypothesized that motor neuron activities exhibit a synergistic organization in complex tasks and therefore that the common input to motor neurons results in a large dimensionality reduction in motor neuron outputs. To test this hypothesis, we factorized the output spike trains of motor neurons innervating 14 intrinsic and extrinsic hand muscles and analyzed the dimensionality of control when healthy individuals exerted isometric forces using seven grip types. We identified four motor neuron synergies, accounting for .70% of the variance of the activity of 54.1 6 12.9 motor neurons, and we identified four functionally similar muscle synergies. However, motor neuron synergies better discriminated individual finger forces than muscle synergies and were more consistent with the expected role of muscles actuating each finger. Moreover, in a few cases, motor neurons innervating the same muscle were active in separate synergies. Our findings suggest a highly divergent net neural inputs to spinal motor neurons from spinal and supraspinal structures, contributing to the dimensionality reduction captured by muscle synergies.

Neuromorphic Decoding of Spinal Motor Neuron Behaviour during Natural Hand Movements for a New Generation of Wearable Neural Interfaces

“…The use of LIF neurons and local spike-driven plasticity rules opens up the possibility of implementing such architecture on neuromorphic chips, leading to an even more efficient online and wearable implementation. We considered muscles in the forearm (extrinsic), to target the use of myoelectric armbands and bracelets for monitoring users' activity [37,38], and in the hand (intrinsic), so far considered mainly in neurophysiology [36,39,40].…”

“…To identify the motor neurons firing patterns of the investigated muscles and track the same motor neurons across multiple tasks, we concatenated the HD-sEMG signals of the recordings relative to each gesture and then decomposed the concatenated HD-sEMG as in [40]. Each group of 64 concatenated HD-sEMG signals corresponding to a recording grid of 64 electrodes was decomposed separately with the CKC algorithm [45] (Fig.…”

Inter-Person Differences in Isometric Coactivations of Triceps Surae and Tibialis Anterior Decrease in Young, but Not in Older Adults After 14 Days of Bed Rest

“…On the other hand, identification of MU discharges from noninvasively recorded high-density EMG (hdEMG) signals offers a detailed insight into the behavior of relatively large number of MUs (Holobar and Zazula, 2007;Negro et al, 2009;Tanzarella et al, 2021). These EMG decomposition techniques identify the MU discharge times and fully remove the effects of MUAPs from the recorded EMG signals (Holobar and Farina, 2021).…”

“…They also offer efficient control of MU identification accuracy (Holobar et al, 2014) and, therefore, control of the quality of the muscle activation estimation. Different approaches to muscle activation estimation from MU discharge patterns were also developed, including Principal Component Analysis of smoothed MU discharge rates (Negro et al, 2009), NMF of smoothed MU discharge rates (Tanzarella et al, 2021) and Cumulative Spike Train (CST) of identified MUs (Farina et al, 2014). Indeed, studies of simultaneous MU behavior in different muscles are increasing (Héroux et al, 2014;de Souza et al, 2018;Kranjec and Holobar, 2019;Davis et al, 2020;Potočnik et al, 2020;Cohen et al, 2021;Tanzarella et al, 2021).…”

“…Different approaches to muscle activation estimation from MU discharge patterns were also developed, including Principal Component Analysis of smoothed MU discharge rates (Negro et al, 2009), NMF of smoothed MU discharge rates (Tanzarella et al, 2021) and Cumulative Spike Train (CST) of identified MUs (Farina et al, 2014). Indeed, studies of simultaneous MU behavior in different muscles are increasing (Héroux et al, 2014;de Souza et al, 2018;Kranjec and Holobar, 2019;Davis et al, 2020;Potočnik et al, 2020;Cohen et al, 2021;Tanzarella et al, 2021). However, to the best of our knowledge, comparisons of MU behavior in a group of simultaneously recorded skeletal muscles of older and young persons are still largely lacking.…”