Biology subjects

Millet, G. Y.

Publications and source records attributed to Millet, G. Y..

3 recordsLinked to original sources

bioRxiv2024

Reliability of transcranial magnetic stimulation-evoked responses on knee extensor muscles during cycling

Transcranial magnetic stimulation (TMS) measures the excitability and inhibition of corticomotor networks. Despite its task-specificity, few studies have used TMS during dynamic movements and the reliability of TMS-derived measures has not been assessed during cycling. This study aimed to evaluate the reliability of motor evoked potentials (MEP) and short- and long-interval intracortical inhibition (SICI and LICI) on vastus lateralis and rectus femoris muscle activity during a fatiguing single-leg cycling task. Nine healthy adults (2 females) performed two identical sessions of counterweighted single-leg cycling at 60% peak power output until failure. Five single-pulses and five short- and long-interval paired pulses delivered to the motor cortex, and two maximal femoral nerve stimulations [maximal M-wave (Mmax)], were delivered during two baseline cycling bouts (unfatigued) and every 5 min throughout cycling (fatigued). When comparing both baseline bouts within the same session, MEP{middle dot}Mmax-1 and LICI (both ICC: >0.9) were rated excellent while SICI was rated good (ICC: 0.7-0.9). At baseline between sessions, in the vastus lateralis, Mmax (ICC: >0.9) and MEP{middle dot}Mmax-1 (ICC: 0.7) demonstrated good reliability, LICI was moderate (ICC: 0.5), and SICI was poor (ICC: 0.3). Across the fatiguing task, Mmax demonstrated excellent reliability (ICC >0.8), MEP{middle dot}Mmax-1 ranged good to excellent (ICC: 0.7-0.9), LICI was moderate to excellent (ICC: 0.5-0.9), and SICI remained poorly reliable (ICC: 0.3-0.6). Overall, these results corroborate the cruciality of retaining mode-specific testing measurements and suggest that during cycling, Mmax, MEP{middle dot}Mmax-1, and LICI measures are reliable whereas SICI, although less reliable across days, can be reliable within the same session.

physiology↗

bioRxiv2022

Wide-pulse electrical stimulation of the quadriceps femoris allows greater maximal evocable torque than conventional stimulation

PurposeThe effectiveness of a neuromuscular electrical stimulation (NMES) program has been shown to be proportional to the maximal evocable torque (MET), which is potentially influenced by pulse characteristics such as duration and frequency. The aim of this study was to compare MET between conventional and wide-pulse NMES at two different frequencies. MethodsMET - expressed as a percentage of maximal voluntary contraction (MVC) torque - and maximal tolerable current intensity (MTCI) were quantified on 71 healthy subjects. The right quadriceps femoris muscle was stimulated with three NMES protocols using different pulse duration/frequency combinations: conventional NMES (0.2 ms/50 Hz; CONV), wide-pulse NMES at 50 Hz (1 ms/50 Hz; WP50) and wide-pulse NMES at 100 Hz (1 ms/100 Hz; WP100). The proportion of subjects reaching the maximal stimulator output (100 mA; MSO) before attaining MTCI was also quantified for each NMES protocol. ResultsThe proportion of subjects attaining MSO was higher for CONV than WP50 and WP100 (30%, 3% and 4%, respectively; p < 0.001). In subjects who did not attain MSO in any protocol, MET was higher for both WP50 and WP100 than for CONV (45% vs 43% vs 39% MVC; p < 0.001). MTCI was lower for both WP50 and WP100 than for CONV (45, 42, and 77 mA, respectively; p < 0.001) and was also lower for WP100 than for WP50. ConclusionWhen compared to conventional NMES, wide-pulse protocols resulted in greater MET, lower MTCI and consequently in a lower proportion of subjects attaining MSO. Overall, this may lead to better NMES training/rehabilitation effectiveness and less practical issues associated with MSO limitations.

physiology↗

bioRxiv2021

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution

Ground reaction forces (GRFs) are important for understanding human movement, but their measurement is generally limited to a laboratory. Previous studies used neural networks to predict GRF waveforms during running from wearable device data, but these predictions are limited to the stance phase of level-ground running. We sought to develop a recurrent neural network capable of predicting continuous normal (perpendicular to surface) GRFs across a range of running speeds and slopes from accelerometer data. 19 subjects ran on a force-measuring treadmill at five slopes (0{degrees}, {+/-}5{degrees}, {+/-}10{degrees}) and three speeds (2.5, 3.33, 4.17 m/s) per slope with sacral- and shoe-mounted accelerometers. We then trained a recurrent neural network to predict normal GRF waveforms frame-by-frame. The predicted versus measured GRF waveforms had an average {+/-} SD RMSE of 0.16 {+/-} 0.04 BW and relative RMSE of 6.4 {+/-} 1.5% across all conditions and subjects. The recurrent neural network predicted continuous normal GRF waveforms across a range of running speeds and slopes with greater accuracy than neural networks implemented in previous studies. This approach may facilitate predictions of biomechanical variables outside the laboratory in near real-time and improves the accurately of quantifying and monitoring external loads experienced by the body when running.

bioengineering↗