Investigation of assumptions underlying current safety guidelines on EM-induced nerve stimulation

Abstract

An intricate network of a variety of nerves is embedded within the complex anatomy of the human body. Although nerves are shielded from unwanted excitation, they can still be stimulated by external electromagnetic sources that induce strongly non-uniform field distributions. Current exposure safety standards designed to limit unwanted nerve stimulation are based on a series of explicit and implicit assumptions and simplifications. This paper demonstrates the applicability of functionalized anatomical phantoms with integrated coupled electromagnetic and neuronal dynamics solvers for investigating the impact of magnetic resonance exposure on nerve excitation within the full complexity of the human anatomy. The impact of neuronal dynamics models, temperature and local hot-spots, nerve trajectory and potential smoothing, anatomical inhomogeneity, and pulse duration on nerve stimulation was evaluated. As a result, multiple assumptions underlying current safety standards are questioned. It is demonstrated that coupled EM-neuronal dynamics modeling involving realistic anatomies is valuable to establish conservative safety criteria.

Numerical modeling of percutaneous auricular vagus nerve stimulation: a realistic 3D model to evaluate sensitivity of neural activation to electrode position

“…The models (T-NEURO) enable the dynamic modeling of EM-induced neuronal activation and inhibition using either complex, multi-compartmental representations of axons, nerves as bundles of axons, and neuronal networks with varying channel dynamics, or generic models. The neuronal tissue models from Sim4Life were validated and used to investigate electromagnetic field interactions with nerves in complex tissue-structure environments [26,27].…”

Functionalized Anatomical Models for Computational Life Sciences

“…In addition to the unique quality of the underlying color-image data and the large number of identified tissues, the distinguishing feature of NEUROMAN is the effort toward adding an extensive tracing of the PNS to the model. The aim is to functionalize the PNS trajectories with electrophysiological neuron models, a concept developed in recent pilot studies (Neufeld et al, 2016b ; Cassara et al, 2017b ). Foreseen applications are manifold and include, e.g., risk assessment of low-frequency exposures from high-power wireless power transfer systems (Reilly and Hirata, 2016 ) or the development of safe MRI sequences with improved contrast and resolution.…”

“…For example, functionalization of anatomical models with nerve trajectories (Figure 1 ) and with neuron electrophysiology models has proven valuable in questioning the assumptions behind current low-frequency exposure safety standards (Neufeld et al, 2016a , b ; Reilly and Hirata, 2016 ) and gaining insights into relevant factors. Only by combining anatomical models that provide the physical environment in which exposure occurs with realistic neuron trajectories and electrophysiologically accurate neuron models it could be shown that (i) the inhomogeneous in vivo field conditions connect with the non-linearity of neural response to elicit action potentials and (ii) the discontinuity of these fields at tissue interfaces can give rise to neurostimulation in passing nerves.…”

Sensitivity analysis of neurodynamic and electromagnetic simulation parameters for robust prediction of peripheral nerve stimulation

“…So et al found that the size, shape, and spatial resolution of the body model have a major impact on the internal E-fields generated (So et al 2004), as does the position of the coil relative to the body model (Pisa et al 2014, So et al 2004). Neufeld et al showed that other simulation aspects such as the relative orientation of the nerve segments and the E-field or the neurodynamic model chosen to describe the nerve membrane dynamics can also have a significant impact on PNS predictions (Neufeld et al 2016). Given the recent successes in using EM field calculation in realistic body models together with neurodynamic simulations and the known dependency of the simulation outcome on important determinants within the simulation (Laakso et al 2014, Neufeld et al 2016, Davids et al 2017, 2018, Mourdoukoutas et al 2018), a systematic analysis of the impact of all key parameters on the predicted PNS thresholds is needed to understand the validity and robustness of the simulation results.…”