Comparison of electrical nerve stimulation, electrical muscle stimulation and magnetic nerve stimulation to assess the neuromuscular function of the plantar flexor muscles.

INTRODUCTION: As it might lead to less discomfort, magnetic nerve stimulation (MNS) is increasingly used as an alternative to electrical stimulation methods. Yet, MNS and electrical nerve stimulation (ENS) and electrical muscle stimulation (EMS) have not been formally compared for the evaluation of plantar flexor neuromuscular function. METHODS: We quantified plantar flexor neuromuscular function with ENS, EMS and MNS in 10 volunteers in fresh and fatigued muscles. Central alterations were assessed through changes in voluntary activation level (VAL) and peripheral function through changes in M-wave, twitch and doublet (PS100) amplitudes. Discomfort associated with 100-Hz paired stimuli delivered with each method was evaluated on a 10-cm visual analog scale. RESULTS: VAL, agonist and antagonist M-wave amplitudes and PS100 were similar between the different methods in both fresh and fatigued states. Potentiated peak twitch was lower in EMS compared to ENS, whereas no difference was found between ENS and MNS for any parameter. Discomfort associated with MNS (1.5 ± 1.4 cm) was significantly less compared to ENS (5.5 ± 1.9 cm) and EMS (4.2 ± 2.6 cm) (p < 0.05). CONCLUSION: When PS100 is used to evaluate neuromuscular properties, MNS, EMS and ENS can be used interchangeably for plantar flexor neuromuscular function assessment as they provide similar evaluation of central and peripheral factors in unfatigued and fatigued states. Importantly, electrical current spread to antagonist muscles was similar between the three methods while discomfort from MNS was much less compared to ENS and EMS. MNS may be potentially employed to assess neuromuscular function of plantar flexor muscles in fragile populations.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.