  Objective: The aim of this study is to propose an adaptive scheme embedded
into an open-source environment for the estimation of the neural activation
extent during deep brain stimulation and to investigate the feasibility of
approximating the neural activation extent by thresholds of the field solution.
Methods: Open-source solutions for solving the field equation in volume
conductor models of deep brain stimulation and computing the neural activation
are embedded into a Python package to estimate the neural activation dependent
on the dielectric tissue properties and axon parameters by employing a
spatially adaptive scheme. Feasibility of the approximation of the neural
activation extent by field thresholds is investigated to further reduce the
computational expense. Results: The varying extents of neural activation for
different patient-specific dielectric properties were estimated with the
adaptive scheme. The results revealed the strong influence of the dielectric
properties of the encapsulation layer in the acute and chronic phase after
surgery. The computational time required to determine the neural activation
extent in each studied model case was substantially reduced. Conclusion: The
neural activation extent is altered by patient-specific parameters. Threshold
values of the electric potential and electric field norm facilitate a
computationally efficient method to estimate the neural activation extent.
Significance: The presented adaptive scheme is able to robustly determine
neural activation extents and field threshold estimates for varying dielectric
tissue properties and axon diameters while reducing substantially the
computational expense.
