Realistic volumetric-approach to simulate transcranial electric stimulation—ROAST—a fully automated open-source pipeline

Abstract

Objective. Research in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subject’s MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each of these steps, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool—realistic volumetric-approach to simulate transcranial electric simulation (ROAST)—is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software and custom scripts to improve segmentation and execute electrode placement. Approach. ROAST combines the segmentation algorithm of SPM12, a Matlab script for touch-up and automatic electrode placement, the finite element mesher iso2mesh and the solver getDP. We compared its performance with commercial FEM software, and SimNIBS, a well-established open-source modeling pipeline. Main results. The electric fields estimated with ROAST differ little from the results obtained with commercial meshing and FEM solving software. We also do not find large differences between the various automated segmentation methods used by ROAST and SimNIBS. We do find bigger differences when volumetric segmentation are converted into surfaces in SimNIBS. However, evaluation on intracranial recordings from human subjects suggests that ROAST and SimNIBS are not significantly different in predicting field distribution, provided that users have detailed knowledge of SimNIBS. Significance. We hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We released ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling.

Dose-controlled tDCS reduces electric field intensity variability at a cortical target site

“…We estimated current flow throughout cortex in individual brains using Realistic Volumetric Approach to Simulate Transcranial Electric Stimulation (ROAST) software package, version 2.7 (https://www.parralab.org/roast/) [26]. ROAST is an end-to-end pipeline that automatically processes individual MRI volumes to generate a 3D rendering of E-field distribution based on user-defined tDCS protocol.…”

“…ROAST accepts a 1mm 3 resolution MRI image, which is segmented using SPM12 (https://www.fil.ion.ucl.ac.uk/spm/) into grey matter, white matter, cerebrospinal fluid (CSF), bone, skin, and air cavities. Automatic touch-up of the segmented images to remove holes is completed using morphological operations and simple heuristics (detailed in [26,30]). Electrodes are then placed on the scalp surface based on user-defined 10-10 coordinates.…”

“…Here we developed a simple and accessible pipeline using current flow modelling [26] and group-level statistics to quantify E-field in the brain when dose-controlling tDCS. Based on intensities in a cortical target (left M1) observed in our cohort, tDCS dose was individualised to produce a fixed cortical E-field in this region across all individuals.…”

“…Reduction of inter-individual variability may be possible using current flow modelling [24][25][26][27]. This provides estimates of E-field across an individual's brain, given a specific electrode montage and stimulator output.…”

“…T1-weighted structural MRIs from 50 randomly selected healthy adults (aged[22][23][24][25][26][27][28][29][30][31][32][33][34][35]23 males, 27 females) were obtained from the Human Connectome Project (HCP) database(https://ida.loni.usc.edu/login.jsp). Images were acquired from a 3.0 T Siemens Connectome Skyra scanner using a standard 32 channel Siemens receiver head coil.…”