OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling

• Published in: PLoS computational biology Vol. 16; no. 7; p. e1008023
• Main Authors: Butenko, Konstantin, Bahls, Christian, Schröder, Max, Köhling, Rüdiger, van Rienen, Ursula
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 06.07.2020; Public Library of Science (PLoS)
• Subjects: Adaptive algorithms; Algorithms; Automatic control; Automation; Biology and Life Sciences; Brain; Brain stimulation; CAD; CAE; Computational neuroscience; Computer aided design; Computer aided engineering; Computer and Information Sciences; Computer programs; Computer simulation; Conductors; Deep brain stimulation; Electric fields; Electric potential; Electrical engineering; Electrodes; Engineering and Technology; Finite element method; Freeware; Geometry; Grid refinement (mathematics); Investigations; Medicine and Health Sciences; Methods; Open source software; Optimization; Physical Sciences; Research and Analysis Methods; Science Policy; Simulation; Software; Source code; Stimulation; Supervision; User interface; Germany
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1008023

In this study, we propose a new open-source simulation platform that comprises computer-aided design and computer-aided engineering tools for highly automated evaluation of electric field distribution and neural activation during Deep Brain Stimulation (DBS). It will be shown how a Volume Conductor Model (VCM) is constructed and examined using Python-controlled algorithms for generation, discretization and adaptive mesh refinement of the computational domain, as well as for incorporation of heterogeneous and anisotropic properties of the tissue and allocation of neuron models. The utilization of the platform is facilitated by a collection of predefined input setups and quick visualization routines. The accuracy of a VCM, created and optimized by the platform, was estimated by comparison with a commercial software. The results demonstrate no significant deviation between the models in the electric potential distribution. A qualitative estimation of different physics for the VCM shows an agreement with previous computational studies. The proposed computational platform is suitable for an accurate estimation of electric fields during DBS in scientific modeling studies. In future, we intend to acquire SDA and EMA approval. Successful incorporation of open-source software, controlled by in-house developed algorithms, provides a highly automated solution. The platform allows for optimization and uncertainty quantification (UQ) studies, while employment of the open-source software facilitates accessibility and reproducibility of simulations.