Biology subjects

Lacourpaille, L.

Publications and source records attributed to Lacourpaille, L..

3 recordsLinked to original sources

bioRxiv2026

Mental fatigue impairs cycling endurance performance and perception of effort, but not muscle activation

Mental fatigue is induced by prolonged engagement in cognitively demanding tasks and impairs endurance performance. The neuropsychophysiological mechanisms underlying this decreased performance remain unclear, with suggestion that mental fatigue may disrupt motor command and consequently muscle activation. We aimed to test this hypothesis in a repeated cross-over design study in which 18 participants completed two experimental sessions involving a time-to-task failure cycling test at 80% of peak power output. Each cycling task was preceded by 1h of a prolonged Stroop task (Stroop condition) or a neutral control task (Control condition). Mental fatigue was assessed using a visual analog scale anchored with "not fatigued at all" and "extremely fatigued. Perception of effort and surface electromyography from ten lower-limb muscles of the right leg were recorded at regular intervals during cycling. Mental fatigue was higher in the Stroop compared to the Control condition (p = .002). Endurance cycling time was shorter in the Stroop than in the Control condition (887 {+/-} 284 s vs. 999 {+/-} 379 s, respectively; -111 {+/-} 160 s, p = .009). No significant differences in electromyography parameters were observed between Stroop and Control conditions, for any muscle (p > .05). Perception of effort was higher in the Stroop condition from the onset of the cycling task (p = .006), and the rate of increase in perception of effort was significantly higher in the Stroop than Control condition (p = .031). Our findings do not support the hypothesis that mental fatigue alters motor control or increases central motor command, as no changes in muscle activation were detected. Conversely, our results reinforce the notion that prolonged cognitive engagement impairs endurance performance primarily through an increased perception of effort. Future research should consider combining surface electromyography with more sensitive neurophysiological techniques to investigate potential subtle changes in motor drive during dynamic, whole-body tasks under mental fatigue.

physiology↗

bioRxiv2023

I-Spin live: An open-source software based on blind-source separation for decoding the activity of spinal alpha motor neurons in real-time

Decoding the activity of individual neural cells during natural behaviours allows neuroscientists to study how the nervous system generates and controls movements. Contrary to other neural cells, the activity of spinal motor neurons can be determined non-invasively (or minimally invasively) from the decomposition of electromyographic (EMG) signals into motor unit firing activities. For some interfacing and neuro-feedback investigations, EMG decomposition needs to be performed in real-time. Here, we introduce an open-source software that performs real-time decoding of motor neurons using a blind-source separation approach for multichannel EMG signal processing. Separation vectors (motor unit filters) are optimised for each motor unit from baseline contractions and then re-applied in real-time during test contractions. In this way, the firing activity of multiple motor neurons can be provided through different forms of visual feedback. We provide a complete framework with guidelines and examples of recordings to guide researchers who aim to study movement control at the motor neuron level. We first validated the software with synthetic EMG signals generated during a range of isometric contraction patterns. We then tested the software on data collected using either surface or intramuscular electrode arrays from five lower limb muscles (gastrocnemius lateralis and medialis, vastus lateralis and medialis, and tibialis anterior). We assessed how the muscle or variation of contraction intensity between the baseline contraction and the test contraction impacted the accuracy of the real-time decomposition. This open-source software provides a set of tools for neuroscientists to design experimental paradigms where participants can receive real-time feedback on the output of the spinal cord circuits.

neuroscience↗

bioRxiv2020

Revealing the unique features of each individual her/his muscle activation signatures

There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear Support Vector Machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision making by the machine learning classification model, a Layer-wise Relevance Propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualising each individuals muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.

neuroscience↗