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

Konstantin Butenko, Christian Bahls, Max Schröder, Rüdiger Köhling and Ursula van Rienen

PLOS Computational Biology, 2020, vol. 16, issue 7, 1-18

Abstract: 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.Author summary: Volume conductor models for the computation of the potential and current distribution resulting from deep brain stimulation can help research to gain a deeper understanding of the underlying processes as well as in optimization studies. On the other hand, they are extremely valuable for patient-specific therapy planning while avoiding side effects as far as possible. Despite existing high-quality models, further potential exists to increase their level of realism, precision and reliability and to allow robust optimization. Our approach enables high-precision, patient- or atlas-based results for deep brain stimulation while simultaneously exploiting different measures to achieve high computational efficiency. In the development of the simulation software, we follow the goals of Open Science—in particular the principles of open-source, open data and reproducibility. In two benchmark examples, one on the human brain, the other on the rat brain, we were able to clearly demonstrate the accuracy and efficiency of our simulation results in comparison to a high-resolution simulation using a commercial software. The developed platform provides both the scientific community and clinicians with a precise yet easy-to-use simulation tool for scientific optimization studies and patient-specific therapy planning in context of deep brain stimulation.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1008023

DOI: 10.1371/journal.pcbi.1008023

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