Comparing and Validating Automated Tools for Individualized Electric Field Simulations in the Human Head

Abstract

Comparing electric field simulations from individualized head models against in-vivo recordings is important for direct validation of computational field modeling for transcranial brain stimulation and brain mapping techniques such as electro-and magnetoencephalography. This also helps to improve simulation accuracy by pinning down the factors having the largest influence on the simulations. Here we compare field simulations from four different automated pipelines, against intracranial voltage recordings in an existing dataset of 14 epilepsy patients. We show that ignoring uncertainty in the simulations leads to a strong bias in the estimated linear relationship between simulated and measured fields. In addition, even though the simulations between the pipelines differ notably, this is not reflected in the correlation with the measurements. We discuss potential reasons for this apparent mismatch and propose a new Bayesian regression analysis of the data that yields unbiased estimates enabling robust conclusions to be reached.

Electric field simulations for transcranial brain stimulation using FEM: an efficient implementation and error analysis

“…We believe that overall simulation errors in the observed range are currently still acceptable, given that other error sources such as segmentation errors [38,45], the uncertainty of the values of the ohmic tissue conductivities [46], putative inaccurate or simplified modeling of the of TMS coil or TES electrode properties and positions [7] can cause errors in or above this range. For example, in a recent study [46], we found standard deviations in maximum electric field values due to uncertainty in tissue conductivities to be around 5% of the mean value in TMS, but around 20% of the mean value in TES, and in another study [45], we found the TES electric fields obtained from different head segmentation tools to vary up to 40%. This suggest that future efforts to increase simulation accuracy will have to commonly account for all these factors, rather than focusing on improving the numerical accuracy alone.…”

Enhanced tES and tDCS computational models by meninges emulation

“…Blind parameter optimization inevitably enhances match; in our principled approach we tested the emulated-CSF value determined systematically in concentric-sphere. The application of this data set is not without noise and methodological nuance [44], and the method of segmentation and other modeling assumption [27] would impact the role of the CSF compartment. In any case, broader consideration of changing modeling pipelines is explicitly outside our scope.…”

Personalized tDCS for Focal Epilepsy—A Narrative Review: A Data-Driven Workflow Based on Imaging and EEG Data

“…For instance, for neurostimulation purposes only, there is SIMNIBS [82] and ROAST [83]. A comparison between these packages has been presented in [84]. The main differences in the results between those two packages were found to be caused by differences in segmentation by the two packages.…”