bioRxiv ScienceSearch

Biology subjects

Casolo, A.

Publications and source records attributed to Casolo, A..

2 recordsLinked to original sources

Why humans are stronger but not faster after isometric strength training: specific neural, not muscular, motor unit adaptations

While maximal force increases following short-term isometric strength training, the rate of force development (RFD) may remain relatively unaffected. The underlying neural and muscular mechanisms during rapid contractions after strength training are largely unknown. Since strength training increases the neural drive to muscles, it may be hypothesized that there are distinct neural or muscular adaptations determining the change in RFD independently of an increase in maximal force. Therefore, we examined motor unit population data during the rapid generation of force before and after four weeks of strength training. We observed that strength training did not change the RFD because it did not influence the number of motor units recruited per second or their initial discharge rate during rapid contractions. While strength training did not change motoneuron behaviour in the force increase phase of rapid contractions, it increased the discharge rate of motoneurons (by [~]4 spikes/s) when reaching the plateau phase ([~]150 ms) of the rapid contractions, determining an increase in maximal force production. Computer simulations with a motor unit model that included neural and muscular properties, closely matched the experimental observations and demonstrated that the lack of change in RFD following training is primarily mediated by an unchanged maximal recruitment speed of motoneurons. These results demonstrate that maximal force and contraction speed are determined by different adaptations in motoneuron behaviour following strength training and indicate that increases in the recruitment speed of motoneurons are required to evoke training-induced increases in RFD.

neuroscience

Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators

There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different submaximal intensities. After manual analysis, 126 {+/-} 5 motor units were retained (range across operators: 119-134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons x 11 contractions/muscles x 4-28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.

neuroscience