Objective: Electroneurography is a well-established diagnostic test for supporting the diagnosis of disorders of myelinated peripheral nerves. Neurophysiological quantities are automatically calculated and are used to determine the pathology of the nerve (axonal damage) or its sheath (myelin damage). Specific differential diagnostic criteria are derived from time-domain normative data, which result primarily from a computer simulation in the early 1990s based on animal data, namely rats. However, the rat signals studied differ significantly from those of humans because of anatomical differences.
Methods: We present a model-based simulation of nerve conduction in healthy and pathological motor nerves. In contrast to earlier simulations, the present model is based on motor unit action potentials extracted from real human measurements facilitating the generation of realistic signals, starting from a conduction velocity distribution. In addition to the modeling of healthy nerves, we model a hereditary peripheral nerve disease as well as an acute and a chronic inflammatory demyelinating condition.
Results: Quantitative signal differences based on standard variables in the time-domain are presented. The findings for the demyelinating conditions demonstrate amplitude reductions of 71% and 65% between the distal and proximal responses, which result from an increase in the variance of the nerve fiber conduction velocities.
Conclusion: The simulation results closely match those of empirical measurements, indicating that the signal model captures relevant pathological mechanisms. An amplitude reduction of more than 50% in demyelinating conditions is in accordance with routine measurements and shows that temporal dispersion is quite well-modeled compared to previous simulation models.
Significance: The simulation outcomes can serve as the basis for an improved pathophysiological understanding of peripheral nerve disorders and should aid neurophysiologists to refine their diagnostic armamentarium resulting in a more precise differential diagnosis.