Biology subjects

Tresch, M. C.

Publications and source records attributed to Tresch, M. C..

4 recordsLinked to original sources

bioRxiv2022

Limb, joint and pelvic kinematic control in the quail coping with step perturbations

Small cursorial birds display remarkable walking skills and can negotiate complex and unstructured terrains with ease. The neuromechanical control strategies necessary to adapt to these challenging terrains are still not well understood. Here, we analyzed the 2D- and 3D pelvic and leg kinematic strategies employed by the common quail to negotiate visible step-up and step-down perturbations of 1 cm, 2.5 cm, and 5 cm. We used biplanar fluoroscopy to accurately describe joint positions in three dimensions and performed semi-automatic landmark localization using deep learning. Quails negotiated vertical perturbations without major problems and rapidly regained steady-state locomotion. When coping with step-up perturbations, the quail mostly adapted the trailing limb to permit the leading leg to step on the elevated substrate in a similar way as it did during level locomotion. When the quail negotiated step-down perturbations, both legs showed significant adaptations. For small and moderate perturbations (not inducing aerial running) the quail kept the function of the distal joints (i.e., their kinematic pattern) largely unchanged during uneven locomotion, and most changes occurred in proximal joints. The hip regulated leg length, while the distal joints maintained the spring-damped limb patterns. However, to negotiate the largest visible step perturbations, more dramatic kinematic alterations were observed. For these large perturbations, all joints contributed to leg lengthening/ shortening in the trailing leg and both the trailing and leading legs stepped more vertically and less abducted. This indicates a shift from a dynamic walking program to strategies that are focused on maximizing safety.

animal behavior and cognition↗

bioRxiv2021

Estimating muscle activation from EMG using deep learning-based dynamical systems models

ObjectiveTo study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded electromyographic (EMG) signals. Common approaches estimate muscle activation independently for each channel or require manual tuning of model hyperparameters to optimally preserve behaviorally-relevant features. ApproachHere, 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 (RNNs) to model the spatial and temporal regularities that underlie multi-muscle activation. Main ResultsWe 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 tested the generality of the approach by applying AutoLFADS to monkey forearm muscle activity from an isometric task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force compared to low-pass or Bayesian filtering. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than other tested approaches. SignificanceUltimately, this method leverages both dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles that can be used for further studies of multi-muscle coordination and its control by upstream brain areas.

neuroscience↗

bioRxiv2019

Coordination amongst quadriceps muscles suggests neural regulation of internal joint stresses, not simplification of task performance

Many studies have demonstrated co-variation between muscle activations during behavior, suggesting that muscles are not controlled independently. According to one common proposal, this co-variation reflects simplification of task performance by the nervous system, so that muscles with similar contributions to task variables are controlled together. Alternatively, this co-variation 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 co-variation patterns in quadriceps muscle activity during locomotion in rats. The three mono-articular quadriceps muscles (vastus medialis, VM; vastus lateralis, VL; vastus intermedius, VI) produce knee extension and so have identical contributions to task performance; the bi-articular rectus femoris (RF) produces an additional hip flexion. Consistent with the proposal that muscle co-variation is related to similarity of muscle actions on task variables, we found that the co-variation between VM and VL was stronger than their co-variations with RF. However, co-variation between VM and VL was also stronger than their co-variations with VI. Since all vastii have identical actions on task variables, this finding suggests that co-variation between muscle activity is not solely driven by simplification of task performance. Instead, the preferentially strong co-variation 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 co-variations and their role in movement control.

neuroscience↗

bioRxiv2018

Adaptation after vastus lateralis denervation in rats suggests neural regulation of joint stresses and strains

In order to produce movements, muscles must act through joints. The translation from muscle force to limb movement is mediated by internal joint structures that permit movement in some directions but constrain it in others. Although muscle forces acting against constrained directions will not affect limb movements, such forces can cause excess stresses and strains in joint structures, leading to pain or injury. In this study, we hypothesized that the central nervous system (CNS) chooses muscle activations to avoid excess joint stresses and strains. We evaluated this hypothesis by examining adaptation strategies after selective paralysis of a muscle acting at the rat knee. We show that the CNS compromises between restoration of task performance and regulation of joint stresses and strains. These results have significant implications to our understanding of the neural control of movements, suggesting that common theories emphasizing task performance are insufficient to explain muscle activations during behaviors.

neuroscience↗