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Skeletal muscles generate force, enabling movement through a series of fast electro-mechanical activations coordinated by the central nervous system. Understanding the underlying mechanism of such fast muscle dynamics is essential in neuromuscular diagnostics, rehabilitation medicine and sports biomechanics. The unique combination of electromyography (EMG) and ultrafast ultrasound imaging (UUI) provides valuable insights into both electrical and mechanical activity of muscle fibers simultaneously, the excitation-contraction (E-C) coupling. In this feasibility study we propose a novel non-invasive method to simultaneously track the propagation of both electrical and mechanical waves in muscles using high-density electromyography and ultrafast ultrasound imaging (5000 fps). Mechanical waves were extracted from the data through an axial tissue velocity estimator based on one-lag autocorrelation. The E-C coupling in electrically evoked twitch contractions of the Biceps Brachii in healthy participants could successfully be tracked. The excitation wave (i.e. action potential) had a velocity of 3.9±0.5ms-1 and the subsequent mechanical (i.e. contraction) wave had a velocity of 3.5±0.9ms-1. The experiment showed evidence that contracting sarcomeres that were already activated by the action potential (AP) pull on sarcomeres that were not yet reached by the AP, which was corroborated by simulated contractions of a newly developed multisegmental muscle fiber model, consisting of 500 sarcomeres in series. In conclusion, our method can track the electromechanical muscle dynamics with high spatio-temporal resolution. Ultimately, characterizing E-C coupling in patients with neuromuscular diseases (e.g. Duchenne or Becker muscular dystrophy) may assess contraction efficiency, monitor the progression of the disease, and determine the efficacy of new treatment options.

OBJECTIVES: System identification techniques have the potential to assess the contribution of the underlying systems involved in standing balance by applying well-known disturbances. We investigated the reliability of standing balance parameters obtained with multivariate closed loop system identification techniques. METHODS: In twelve healthy elderly balance tests were performed twice a day during three days. Body sway was measured during two minutes of standing with eyes closed and the Balance test Room (BalRoom) was used to apply four disturbances simultaneously: two sensory disturbances, to the proprioceptive and the visual system, and two mechanical disturbances applied at the leg and trunk segment. Using system identification techniques, sensitivity functions of the sensory disturbances and the neuromuscular controller were estimated. Based on the generalizability theory (G theory), systematic errors and sources of variability were assessed using linear mixed models and reliability was assessed by computing indexes of dependability (ID), standard error of measurement (SEM) and minimal detectable change (MDC). RESULTS: A systematic error was found between the first and second trial in the sensitivity functions. No systematic error was found in the neuromuscular controller and body sway. The reliability of 15 of 25 parameters and body sway were moderate to excellent when the results of two trials on three days were averaged. To reach an excellent reliability on one day in 7 out of 25 parameters, it was predicted that at least seven trials must be averaged. CONCLUSION: This study shows that system identification techniques are a promising method to assess the underlying systems involved in standing balance in elderly. However, most of the parameters do not appear to be reliable unless a large number of trials are collected across multiple days. To reach an excellent reliability in one third of the parameters, a training session for participants is needed and at l

Introduction: Small nerve fiber dysfunction is an early feature of diabetic neuropathy. There is a strong clinical need for a non-invasive method to assess small nerve fiber function. Small nerve fibers mediate axon reflex-related vasodilation and play an important role in thermoregulation. Assessing the reflex vasodilation after local heating might elucidate some aspects of small fiber functioning. In this study, we determined the reproducibility of the reflex vasodilation after short local heating in healthy subjects, assessed with thermal imaging and laser Doppler imaging. Methods: Healthy subjects underwent six heating rounds in one session (protocol I, N = 10) or spread over two visits (protocol II, N = 20). Reflex vasodilation was elicited by heating the skin to 42 degrees C with an infrared lamp. Skin temperature and skin blood flow were recorded during heating and recovery with a thermal imaging camera and a laser Doppler imager. Skin temperature curves were fitted with a mathematical model to describe the heating and recovery phase with time constant tau (tau(Heat) and tau(Cool1)). Results: The reproducibility of tau within a session was moderate to excellent (intra-class correlation coefficient 0.42-0.86) and good (0.71-0.72) between different sessions. Within one session the differences in tauHeat were small (bias +/ -SD -1.3 +/- 18.9 s); the bias between two visits was -1.2 +/- 12.2 s. For tau(Cool1) the differences were also small, 1.4 +/- 6.6 s within a session and between visits -1.4 +/- 11.6 s. Conclusions: The heat induced axon reflex-related vasodilation, assessed with thermal imaging and laser Doppler imaging, was reproducible both within a session and between different sessions. Tau describes the temporal profile in one parameter and represents the effects of all changes including blood flow and as such, is an indicator of the vasodilator function. Tau(Heat) and tau(Cool1) can accurately describe the dynamics of the axon reflex-related vasodilato