Realistic vOlumetric-Approach to Simulate Transcranial Electric Stimulation — ROAST — a fully automated open-source pipeline

• Published in: bioRxiv
• Main Authors: Huang, Yu, Datta, Abhishek, Marom Bikson, Parra, Lucas C
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 06.12.2018; Cold Spring Harbor Laboratory
• Edition: 1.3
• Subjects: Anatomy; Automation; Biophysics; Computational neuroscience; Computer programs; Electric fields; Electrical stimuli; Electrodes; Image processing; Magnetic resonance imaging; NMR; Nuclear magnetic resonance; Open source software; Segmentation; Software; finite element method; current-flow model; brain segmentation; transcranial electrical stimulation
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/217331

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 the 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 step, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool -- 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. The electric field estimated with the open-source tools used by 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, a well-established open-source modeling pipeline. However, we do find large differences when volumetric segmentation are converted into surfaces that are used in SimNIBS to generate volumetric meshes. Evaluation on intracranial recordings from human subjects suggests that ROAST outperforms newer versions of SimNIBS in predicting field distribution and magnitudes, but that an older version of SimNIBS performs similarly. We hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We release ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling at https://www.parralab.org/roast/. Footnotes * More comparisons are added for segmentation and electric field simulation between ROAST, commercial software, and SimNIBS. Also they are compared with actual recording data to evaluate their performance.