Levine, J. T. | Search bioRxiv

bioRxiv2024

Modulation of Common Synaptic Inputs and Motor Unit Recruitment Threshold in TricepsSurae Muscles: Effects of Ankle Position

The objective of this study was to investigate how altering muscle length by changing ankle position affects force control, motor unit recruitment, and motor unit coherence within and across triceps surae muscles. Sixteen healthy young adults performed isometric plantarflexion (PF) at three ankle positions with the ankle plantarflexed at 20{degrees} (PF20{degrees}), at the neural position (PF0{degrees}) and dorsiflexed at 20{degrees} (DF20{degrees}). High-density surface electromyography was used to record the medial and lateral heads of the gastrocnemius muscle (GM and GL), and the medial and lateral portions of the soleus muscle (SL and SM). Motor unit cumulative spike trains (MUCST) were used to calculate intramuscular, intermuscular, and Force-MUCST coherence in the delta (0-5 Hz), alpha (5-15 Hz), and beta (15-35 Hz) frequency bands. Changing the ankle position from a shortened to lengthened position resulted in increased GM-SM coherence in the delta and alpha bands and improved force steadiness, while intramuscular coherence and Force-MUCST coherence decreased in the alpha band. However, there were minimal changes in beta band coherence. Motor unit recruitment thresholds of GM and SL were reduced with muscle lengthening, while GL showed greater thresholds at lengthened positions, and SM was unaffected by ankle position. These findings highlight the role of inhibitory inputs associated with changes in muscle length and the modulation of these inputs by neighboring synergistic muscles. This study reveals a neuromuscular control strategy that modulates common synaptic inputs and motor unit recruitment of triceps surae to maintain force output during isometric plantarflexions at varying muscle lengths. Key pointsO_LIWhen comparing isometric contractions at different ankle joint angles, a lengthened position resulted in greater shared common inputs between the medial head of the gastrocnemius and the medial compartment of the soleus in delta and alpha frequency bands during submaximal force production. C_LIO_LIIncreased muscle length resulted in improved force steadiness, as well as reduced common synaptic inputs within each triceps surae muscle and coupling between force output and neural drive in the alpha band. C_LIO_LIShifting the ankle position from a shortened to a lengthened position resulted in a reduced motor unit recruitment threshold of GM and SL. However, GL exhibited an opposite pattern, showing higher recruitment thresholds at lengthened positions, while SM was not affected by changes in muscle length. C_LIO_LIThis study reveals a neuromuscular control strategy that integrates afferent feedback and muscle mechanical properties while modulating common descending inputs to synergistic plantarflexors. C_LI

neuroscience↗

bioRxiv2022

A deep CNN framework for neural drive estimation from HD-EMG across contraction intensities and joint angles

ObjectivePrevious studies have demonstrated promising results in estimating the neural drive to muscles, the net output of all motoneurons that innervate the muscle, using high-density electromyography (HD-EMG) for the purpose of interfacing with assistive technologies. Despite the high estimation accuracy, current methods based on neural networks need to be trained with specific motor unit action potential (MUAP) shapes updated for each condition (i.e., varying muscle contraction intensities or joint angles). This preliminary step dramatically limits the potential generalization of these algorithms across tasks. We propose a novel approach to estimate the neural drive using a deep convolutional neural network (CNN), which can identify the cumulative spike train (CST) through general features of MUAPs from a pool of motor units. MethodsWe recorded HD-EMG signals from the gastrocnemius medialis muscle under three isometric contraction scenarios: 1) trapezoidal contraction tasks with different intensities, 2) contraction tasks with a trapezoidal or sinusoidal torque target, and 3) trapezoidal contraction tasks at different ankle angles. We applied a convolutive blind source separation (BSS) method to decompose HD-EMG signals to CST and segmented both signals into windows to train and validate the deep CNN. Then, we optimized the structure of the deep CNN and validated its generalizability across contraction tasks within each scenario. ResultsWith the optimal configuration for the HD-EMG data window (overlap of 20 data points and window length of 40 data points), the deep CNN estimated the CST close to that from BSS, with a correlation coefficient higher than 0.96 and normalized root-mean-square-error lower than 7% with respect to the BSS (golden standard) within each scenario. ConclusionThe proposed deep CNN framework can utilize data from different contraction tasks (e.g., different intensities), learn general features of MUAP variants, and estimate the neural drive for other contraction tasks. SignificanceWith the proposed deep CNN, we could potentially build a neuraldrive-based human-machine interface that is generalizable to different contraction tasks without retraining.

bioengineering↗