Biology subjects

Chambellant, F.

Publications and source records attributed to Chambellant, F..

4 recordsLinked to original sources

bioRxiv2024

Variations in clustering of multielectrode local field potentials in the motor cortex of macaque monkeys during a reach-and-grasp task

There is experimental evidence of varying correlation among the elements of the neuromuscular system over the course of the reach-and-grasp task. Several neuromuscular disorders are accompanied by anomalies in muscular coupling during the task. The aim of this study was to investigate if modifications in correlations and clustering can be detected in the Local Field Potential (LFP) recordings of the motor cortex during the task. To this end, we analyzed the LFP recordings from a previously published study on monkeys which performed a reach-and-grasp task for targets with a vertical or horizontal orientation. LFP signals were recorded from the motor and premotor cortex of macaque monkeys as they performed the task. We found very robust changes in the correlations of the multielectrode LFP recordings which corresponded to task epochs. Mean LFP correlation increased significantly during reaching and then decreased during grasp. This pattern was very robust for both left and right arm reaches irrespective of target orientation. A hierarchical cluster analysis supported the same conclusion - a decreased number of clusters during reach followed by an increase for grasp. A sliding window computation of the number of clusters was performed to probe the predictive capacities of these LFP clusters for upcoming task events. For a very high percentage of trials (95.3%), there was a downturn in cluster number following the Pellet Drop (GO signal) which reached a minimum shortly preceding the Start of grasp, hence indicating that cluster analyses of LFP signals could provide online indications of the Start of grasp.

neuroscience↗

bioRxiv2024

Targeting a specific motor control process reveals an age-related compensation that adapts movement to gravity environment

As the global population ages, it is crucial to understand sensorimotor compensation mechanisms. These mechanisms are thought to enable older adults to remain in good physical health, but despite important research efforts, they remain essentially chimeras. A major problem with their identification is the ambiguous interpretation of age-related alterations. Whether a change reflects deterioration or compensation is difficult to determine. Here we compared the electromyographic and kinematic patterns of different motor tasks in younger (n = 20; mean age = 23.6 years) and older adults (n = 24; mean age = 72 years). Building on the knowledge that humans take advantage of gravity effects to minimize their muscle effort, we probed the ability of younger and older adults to plan energetically efficient movement during arm-only and whole-body movements. In line with previous studies and compared to younger adults, muscle activation patterns revealed that older adults used a less efficient movement strategy during whole-body movement tasks. We found that this age-related alteration was task-specific. It did not affect arm movements, thereby supporting the hypothesis that healthy older adults maintain the ability to plan energetically efficient movements. More importantly, we found that the reduced whole-body movement efficiency was correlated with kinematic measures of balance control (i.e., the center-of-mass movement amplitude and speed). The more efficient the movement strategy, the more challenging the balance. Overall, these results suggest that reduced movement efficiency in healthy older adults does not reflect a deterioration but rather a compensation process that adapts movement strategy to the task specificities. When balance is at stake, healthy older adults prefer stability to energy efficiency.

neuroscience↗

bioRxiv2023

Tuning for Pointing Direction in Phasic Muscular Activity: Insights From Machine Learning

Arm movements in our daily lives have to be adjusted for several factors in response to the demands of the environment, for example, speed, direction or distance. Previously, we had shown that arm movement kinematics is optimally tuned to take advantage of gravity effects and minimize muscle effort in various pointing directions and gravity contexts (Gaveau et al., 2016). Here we build upon these results and focus on muscular adjustments. We used Machine Learning to analyze the ensemble activities of multiple muscles recorded during pointing in various directions. The advantage of such a technique would be the observation of patterns in collective muscular activity that may not be noticed using univariate statistics. By providing an index of multimuscle activity, the Machine Learning analysis brought to light several features of tuning for pointing direction. In attempting to trace tuning curves, all comparisons were done with respects to pointing in the horizontal, gravity free plane. We demonstrated that tuning for direction does not take place in a uniform fashion but in a modular manner in which some muscle groups play a primary role. The antigravity muscles were more finely tuned to pointing direction than the gravity muscles. Of note, was their tuning during the first half of downward pointing. As the antigravity muscles were deactivated during this phase, it supported the idea that deactivation is not an on-off function but is tuned to pointing direction. Further support for the tuning of the portions of the phasic EMG containing only negative activity was provided by progressively improving classification accuracies with increasing angular distance from the horizontal. Overall, these results show that the motor system tunes muscle commands to exploit gravity effects and reduce muscular effort. It quantitatively demonstrates that phasic EMG negativity is an essential feature of muscle control.

neuroscience↗

bioRxiv2022

Too Much Information Is No Information: How Machine Learning and Feature Selection Could Help in Understanding the Motor Control of Pointing

The aim of this study was to develop the use of Machine Learning techniques as a means of multivariate analysis in studies of motor control. These studies generate a huge amount of data, the analysis of which continues to be largely univariate. We propose the use of machine learning classification and feature selection as a means of uncovering feature combinations that are altered between conditions. High dimensional electromyograms (EMG) vectors were generated as several arm and trunk muscles were recorded while subjects pointed at various angles above and below the gravity neutral horizontal plane. We used Linear Discriminant Analysis (LDA) to carry out binary classifications between the EMG vectors for pointing at a particular angle, versus pointing at the gravity neutral direction. Classification success provided a composite index of muscular adjustments for various task constraints - in this case, pointing angles. In order to find the combination of features that were significantly altered between task conditions, we conducted a post classification feature selection i.e. investigated which combination of features had allowed for the classification. Feature selection was done by comparing the representations of each category created by LDA for the classification. In other words computing the difference between the representations of each class. We propose that this approach will help with comparing high dimensional EMG patterns in two ways; i) quantifying the effects of the entire pattern rather than using single arbitrarily defined variables and ii) identifying the parts of the patterns that convey the most information regarding the investigated effects.

neuroscience↗