Table of Contents
Journal of Computational Medicine
Volume 2016 (2016), Article ID 9826596, 15 pages
Research Article

An Object-Oriented Framework for Versatile Finite Element Based Simulations of Neurostimulation

1Mathematics Department, Rowan University, Glassboro, NJ 08028, USA
2Mathematics Department, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

Received 28 May 2015; Revised 27 August 2015; Accepted 21 October 2015

Academic Editor: Camillo Porcaro

Copyright © 2016 Edward T. Dougherty and James C. Turner. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Computational simulations of transcranial electrical stimulation (TES) are commonly utilized by the neurostimulation community, and while vastly different TES application areas can be investigated, the mathematical equations and physiological characteristics that govern this research are identical. The goal of this work was to develop a robust software framework for TES that efficiently supports the spectrum of computational simulations routinely utilized by the TES community and in addition easily extends to support alternative neurostimulation research objectives. Using well-established object-oriented software engineering techniques, we have designed a software framework based upon the physical and computational aspects of TES. The framework’s versatility is demonstrated with a set of diverse neurostimulation simulations that (i) reinforce the importance of using anisotropic tissue conductivities, (ii) demonstrate the enhanced precision of high-definition stimulation electrodes, and (iii) highlight the benefits of utilizing multigrid solution algorithms. Our approaches result in a framework that facilitates rapid prototyping of real-world, customized TES administrations and supports virtually any clinical, biomedical, or computational aspect of this treatment. Software reuse and maintainability are optimized, and in addition, the same code can be effortlessly augmented to provide support for alternative neurostimulation research endeavors.