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Sport-related injuries to articular structures often alter the sensory information conveyed by joint structures to the nervous system. However, the role of joint sensory afferents in motor control is still unclear. Here, we evaluate the role of knee joint sensory afferents in the control of quadriceps muscles, hypothesizing that such sensory information modulates control strategies that limit patellofemoreal joint loading. We compared locomotor kinematics and muscle activity before and after inhibition of knee sensory afferents by injection of lidocaine into the knee capsule of rats. We evaluated whether this inhibition reduced the strength of correlation between the activity of vastus medialis (VM) and vastus lateralis (VL) both across strides and within each stride, coordination patterns that limit net mediolateral patellofemoral forces. We also evaluated whether this inhibition altered correlations among the other quadriceps muscle activity, the time-profiles of individual EMG envelopes, or movement kinematics. Neither the EMG envelopes nor limb kinematics was affected by the inhibition of knee sensory afferents. This perturbation also did not affect the correlations between VM and VL, suggesting that the regulation of patellofemoral joint loading is mediated by different mechanisms. However, inhibition of knee sensory afferents caused a significant reduction in the correlation between vastus intermedius (VI) and both VM and VL across, but not within, strides. Knee joint sensory afferents may therefore modulate the coordination between the vasti muscles but only at coarse time scales. Injuries compromising joint afferents might result in altered muscle coordination, potentially leading to persistent internal joint stresses and strains.NEW & NOTEWORTHY Sensory afferents originating from knee joint receptors provide the nervous system with information about the internal state of the joint. In this study, we show that these sensory signals are used to modulate t

Objective. To study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. One approach is to try extracting the underlying neural command signal to muscles by applying latent variable modeling methods to electromyographic (EMG) recordings. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded EMG signals. Common approaches estimate each muscle's activation independently or require manual tuning of model hyperparameters to preserve behaviorally-relevant features. Approach. Here, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate muscle activation from multi-muscle EMG signals. AutoLFADS uses recurrent neural networks to model the spatial and temporal regularities that underlie multi-muscle activation. Main results. We first tested AutoLFADS on muscle activity from the rat hindlimb during locomotion and found that it dynamically adjusts its frequency response characteristics across different phases of behavior. The model produced single-trial estimates of muscle activation that improved prediction of joint kinematics as compared to low-pass or Bayesian filtering. We also applied AutoLFADS to monkey forearm muscle activity recorded during an isometric wrist force task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than were other tested approaches. Significance. This method leverages dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles. Ultimately, the approach can be used for further studies of multi-muscle coordination and it

Many studies have demonstrated covariation between muscle activations during behavior, suggesting that muscles are not controlled independently. According to one common proposal, this covariation reflects simplification of task performance by the nervous system so that muscles with similar contributions to task variables are controlled together. Alternatively, this covariation might reflect regulation of low-level aspects of movements that are common across tasks, such as stresses within joints. We examined these issues by analyzing covariation patterns in quadriceps muscle activity during locomotion in rats. The three monoarticular quadriceps muscles (vastus medialis [VM], vastus lateralis [VL], and vastus intermedius [VI]) produce knee extension and so have identical contributions to task performance; the biarticular rectus femoris (RF) produces an additional hip flexion. Consistent with the proposal that muscle covariation is related to similarity of muscle actions on task variables, we found that the covariation between VM and VL was stronger than their covariations with RF. However, covariation between VM and VL was also stronger than their covariations with VI. Since all vastii have identical actions on task variables, this finding suggests that covariation between muscle activity is not solely driven by simplification of overt task performance. Instead, the preferentially strong covariation between VM and VL is consistent with the control of internal joint stresses: Since VM and VL produce opposing mediolateral forces on the patella, the high positive correlation between their activation minimizes the net mediolateral patellar force. These results provide important insights into the interpretation of muscle covariations and their role in movement control.

According to the model of muscle synergies, the central nervous system (CNS) is organised in a modular structure, such that any muscle activation can be produced as a linear superposition of predefined time-varying profiles (i.e. synergies). This organisation might contribute to simplify the control of the musculoskeletal apparatus. Taking inspiration from these findings, we propose a method to identify the synergies that can be used to control a given dynamical system for the task of tracking a set of trajectories. Further, we show how the same approach can be applied to assess the impact of the number of synergies on the performance of the control method. From the theoretical point of view, we provide a novel interpretation of synergies inspired by the Karhunen-Loève decomposition; furthermore, our method suggests that the quality of a set of synergies should be measured in task space rather then in input space.