Coupled simulations and parameter inversion for neural system and electrophysiological muscle models

Author:

Homs‐Pons Carme1,Lautenschlager Robin2ORCID,Schmid Laura3ORCID,Ernst Jennifer4,Göddeke Dominik25,Röhrle Oliver35ORCID,Schulte Miriam15

Affiliation:

1. Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany

2. Institute of Applied Analysis and Numerical Simulation University of Stuttgart Stuttgart Germany

3. Institute for Modeling and Simulation of Biomechanical Systems University of Stuttgart Stuttgart Germany

4. Department of Trauma Surgery Hannover Medical School Hannover Germany

5. Stuttgart Center for Simulation Science University of Stuttgart Stuttgart Germany

Abstract

AbstractThe functioning of the neuromuscular system is an important factor for quality of life. With the aim of restoring neuromuscular function after limb amputation, novel clinical techniques such as the agonist‐antagonist myoneural interface (AMI) are being developed. In this technique, the residual muscles of an agonist‐antagonist pair are (re‐)connected via a tendon in order to restore their mechanical and neural interaction. Due to the complexity of the system, the AMI can substantially profit from in silico analysis, in particular to determine the prestretch of the residual muscles that is applied during the procedure and determines the range of motion of the residual muscle pair. We present our computational approach to facilitate this. We extend a detailed multi‐X model for single muscles to the AMI setup, that is, a two‐muscle‐one‐tendon system. The model considers subcellular processes as well as 3D muscle and tendon mechanics and is prepared for neural process simulation. It is solved on high performance computing systems. We present simulation results that show (i) the performance of our numerical coupling between muscles and tendon and (ii) a qualitatively correct dependence of the range of motion of muscles on their prestretch. Simultaneously, we pursue a Bayesian parameter inference approach to invert for parameters of interest. Our approach is independent of the underlying muscle model and represents a first step toward parameter optimization, for instance, finding the prestretch, to be applied during surgery, that maximizes the resulting range of motion. Since our multi‐X fine‐grained model is computationally expensive, we present inversion results for reduced Hill‐type models. Our numerical results for cases with known ground truth show the convergence and robustness of our approach.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Wiley

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