Model-based deconvolution of a force signal to estimate motor unit twitch parameters under low-force isometric contractions DOI Creative Commons
Robin Rohlén, Jan Celichowski

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Май 17, 2024

Abstract Muscle force generation and human movement are organised by the central nervous system executed peripheral muscle fibres through molecular electrical mechanisms. Over last half-century, attempts have been made to elucidate these mechanisms in vivo, primarily focusing on motor unit (MU) activity because of its role as smallest voluntarily contractible unit. Although it is firmly established that controls modulating MU activity, yet possible distinguish between activities slow- fast-twitch MUs non-invasively, which important for rehabilitation diagnostic purposes. different methods exist extract twitch parameters from a signal, no method can accurately identify single given spike train. We addressed this problem developing model-based deconvolution method. evaluated using MU-based recruitment model under isometric contractions tested experimental data. found provide non-biased average parameter estimates with low variance latest recruited MUs, irrespective contraction level. It estimate when underlying comprise unequal successive profiles, signal has lower signal-to-noise ratios, or train includes missed firings at cost slightly increased bias variance. Finally, provides align expected characteristics conditions. To conclude, may be used study slow fast neuromuscular diagnostics. Author Summary generate specific muscle, brain plans sends signals spinal cord via neurons, each communicates set fibres. Together, neuron called In literature, neural received much attention, whereas mechanical force-generating less due limitations current methods. By extracting connected type one use information diagnostics humans. Here, we proposed profile during high levels. This

Язык: Английский

The decoding of extensive samples of motor units in human muscles reveals the rate coding of entire motoneuron pools DOI Creative Commons
Simon Avrillon, François Hug, Roger M. Enoka

и другие.

eLife, Год журнала: 2024, Номер 13

Опубликована: Май 9, 2024

Movements are performed by motoneurons transforming synaptic inputs into an activation signal that controls muscle force. The control emerges from interactions between ionotropic and neuromodulatory to motoneurons. Critically, these vary across motoneuron pools differ muscles. To provide the most comprehensive framework date of motor unit activity during isometric contractions, we identified firing extensive samples units in tibialis anterior (129 ± 44 per participant; n=8) vastus lateralis (130 63 muscles contractions up 80% maximal From this unique dataset, rate coding each was characterised as relation its instantaneous applied force, with assumption linear increase force reflects a proportional net excitatory received motoneuron. This natural logarithm function comprised two stages. initial stage marked steep acceleration rate, which greater for low- than medium- high-threshold units. second high- low-threshold Changes were largely non-linear ramp-up ramp-down phases task, but significant prolonged only evident medium-threshold Contrary what is usually assumed, our results demonstrate can follow large variety trends pool. neural perspective, findings indicate how use gain transform limited bandwidths intended

Язык: Английский

Процитировано

2
Model-based deconvolution of a force signal to estimate motor unit twitch parameters under low-force isometric contractions DOI Creative Commons
Robin Rohlén, Jan Celichowski

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Май 17, 2024

Abstract Muscle force generation and human movement are organised by the central nervous system executed peripheral muscle fibres through molecular electrical mechanisms. Over last half-century, attempts have been made to elucidate these mechanisms in vivo, primarily focusing on motor unit (MU) activity because of its role as smallest voluntarily contractible unit. Although it is firmly established that controls modulating MU activity, yet possible distinguish between activities slow- fast-twitch MUs non-invasively, which important for rehabilitation diagnostic purposes. different methods exist extract twitch parameters from a signal, no method can accurately identify single given spike train. We addressed this problem developing model-based deconvolution method. evaluated using MU-based recruitment model under isometric contractions tested experimental data. found provide non-biased average parameter estimates with low variance latest recruited MUs, irrespective contraction level. It estimate when underlying comprise unequal successive profiles, signal has lower signal-to-noise ratios, or train includes missed firings at cost slightly increased bias variance. Finally, provides align expected characteristics conditions. To conclude, may be used study slow fast neuromuscular diagnostics. Author Summary generate specific muscle, brain plans sends signals spinal cord via neurons, each communicates set fibres. Together, neuron called In literature, neural received much attention, whereas mechanical force-generating less due limitations current methods. By extracting connected type one use information diagnostics humans. Here, we proposed profile during high levels. This

Язык: Английский

Процитировано

0