Biology subjects

Alessandro, C.

Publications and source records attributed to Alessandro, C..

6 recordsLinked to original sources

bioRxiv2026

Adaptation to postural perturbations under fatigue produces persistent changes in neuromuscular coordination

Postural control depends on the ability to adapt motor responses to changing environmental and physiological conditions. Neuromuscular fatigue (NMF) is known to alter balance and muscle activation patterns, yet its effects on motor adaptation during whole-body postural tasks and on the persistence of learned strategies remain unclear. This study investigated whether localized NMF of the ankle dorsiflexors influences adaptation to a novel postural perturbation task and whether learning under fatigue induces persistent changes during subsequent re-exposure. Twenty-five healthy young adults were assigned to either a fatigue or no-fatigue group and completed two experimental sessions separated by 48-72 h allowing recovery from acute fatigue for fatigued group. Participants adapted to repeated mechanical perturbations while standing upright, while ground reaction forces and electromyographic activity of lower-limb muscles were recorded. NMF did not impair overall adaptation performance, as both groups exhibited similar reductions in performance error across practice. However, participants exposed to fatigue exhibited altered postural recovery dynamics, characterized by a reduced return toward the initial posture following perturbation release. These differences persisted during re-exposure on the subsequent day, despite the absence of acute fatigue. In parallel, NMF modified muscle activation and coactivation patterns involving both fatigued and non-fatigued muscles, several of which were retained during re-exposure. These findings indicate that the central nervous system preserves successful adaptation to postural perturbations under fatigue by reorganizing neuromuscular coordination and stabilization strategies. Learning under fatigue therefore influences not only immediate motor execution, but also shapes the next-session persistence of postural control strategies.

neuroscience↗

bioRxiv2022

Dual STDP processes at Purkinje cells contribute to distinct improvements in accuracy and vigor of saccadic eye movements.

Saccadic eye-movements play a crucial role in visuo-motor control by allowing rapid foveation onto new targets. However, the neural processes governing saccades adaptation are not fully understood. Saccades, due to the short-time of execution (20-100 ms) and the absence of sensory information for online feedback control, must be controlled in a ballistic manner. Incomplete measurements of the movement trajectory, such as the visual end-point error, are supposedly used to form internal predictions about the movement kinematics resulting in predictive control. In order to characterize the synaptic and neural circuit mechanisms underlying predictive saccadic control, we have reconstructed the saccadic system in a digital controller embedding a spiking neural network of the cerebellum with spike timing-dependent plasticity (STDP) rules driving parallel fiber - Purkinje cell long-term potentiation and depression (LTP and LTD). This model implements a control policy based on a dual plasticity mechanism, resulting in the identification of the roles of LTP and LTD in optimizing saccade movement control: it turns out that LTD regulates the accuracy and LTP the speed (vigor) of the ballistic eye movement. The control policy also required cerebellar PCs to be divided into two subpopulations, characterized by burst or pause responses. To our knowledge, this is the first model that explains in mechanistic terms the accuracy and vigor regulation of ballistic eye movements in forward mode exploiting spike-timing to regulate firing in different populations of the neuronal network. This elementary model of saccades could be extended and applied to other more complex cases in which single jerks are concatenated to compose articulated and coordinated movements. Author summaryIt has been suggested that the cerebellum plays a crucial role in oculomotor adaptation. Computationally, the cerebellum is described as a supervised learner whose activity can be adjusted by synaptic changes proportional to the amount of mismatch between expected and actual movements outcomes (sensorimotor-errors). However, the spike-timing-dependent-plasticity (STDP) underlying adaptation has been so far modeled in behaviors where the error is continuously available as tracking-error. Such models depend on detailed tracking-errors to improve movement quality. We asked if the cerebellum can maintain good motor control even if the error is not completely available? This is important considering that error-dependent-STDP is only a subset among a family of STDP processes in the cerebellum. Moreover, even the physiological signals that were generally thought to carry sensorimotor-errors (called complex-spikes) are shown to encode multiple types of information regarding the movement. In this context, we characterize the role of cerebellar STDPs in saccade control, where the error information is constrained because of sensory suppression. We show that even in the absence of detailed error, the cerebellum can leverage two of its STDPs to increase movement quality. Hence, we emphasize the need to go beyond error-centric view to understand how the cerebellum improves motor behavior.

neuroscience↗

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

Cardiovascular responses to antigravity muscle loading during head-down tilt at rest and after dynamic exercises

The physiological processes underlying hemodynamic homeostasis can be modulated by muscle activity and gravitational loading. The effects of antigravity muscle activity on cardiovascular regulation has been observed during orthostatic stress. Here, we evaluated such effects during head-down tilt (HDT). In this posture, the gravitational gradient along the body is different than in upright position, leading to increased central blood volume and reduced venous pooling. We compared the cardiovascular signals obtained with and without antigravity muscle loading during HDT in healthy human subjects, both at rest and during recovery from leg-press exercises. Further, we compared such cardiovascular responses to those obtained during upright position. We found that loading the antigravity muscles during HDT at rest led to significantly higher values of arterial blood pressure than without muscle loading, and restored systolic values to those observed during upright posture. Maintaining muscle loading post-exercise altered the short-term cardiovascular responses, but not the values of the signals five minutes after the exercise. These results demonstrate that antigravity muscle activity modulates cardiovascular regulation during HDT. This modulation should therefore be considered when interpreting cardiovascular responses to conditions that affect both gravity loading and muscle activity, for example bed rest or microgravity.

physiology↗

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↗