ArticleJournal of neural engineering2016
Changing head model extent affects finite element predictions of transcranial direct current stimulation distributions.
Article in Journal of neural engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Machine learning and individual variability in electrical field characteristics predict tDCS treatment response for anxiety in older adults in the ACT trial.Frontiers in human neuroscience · 2026Article
- Validation of conductivity tensor imaging against diffusion tensor magnetic resonance electrical impedance tomography.Scientific reports · 2024Article
- Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Modeling for Electromagnetic Characterization, Prediction, and Reconstruction.Advances in experimental medicine and biology · 2022Article
- Article
- i-SATA: A MATLAB based toolbox to estimate current density generated by transcranial direct current stimulation in an individual brain.Journal of neural engineering · 2020Article
- A flexible workflow for simulating transcranial electric stimulation in healthy and lesioned brains.PloS one · 2020Article
- Benchmarking transcranial electrical stimulation finite element models: a comparison study.Journal of neural engineering · 2019Article
- Methods to Compare Predicted and Observed Phosphene Experience in tACS Subjects.Neural plasticity · 2018Article
- Non-invasive Brain Stimulation: Probing Intracortical Circuits and Improving Cognition in the Aging Brain.Frontiers in aging neuroscience · 2018Review
- Modeling transcranial electrical stimulation in the aging brain.Brain stimulationArticle
- Methods to monitor accurate and consistent electrode placements in conventional transcranial electrical stimulation.Brain stimulationArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveIn this study, we determined efficient head model sizes relative to predicted current densities in transcranial direct current stimulation (tDCS). APPROACH: Efficiency measures were defined based on a finite element (FE) simulations performed using nine human head models derived from a single MRI data set, having extents varying from 60%-100% of the original axial range. Eleven tissue types, including anisotropic white matter, and three electrode montages (T7-T8, F3-right supraorbital, Cz-Oz) were used in the models. MAIN
resultsReducing head volume extent from 100% to 60%, that is, varying the model's axial range from between the apex and C3 vertebra to one encompassing only apex to the superior cerebellum, was found to decrease the total modeling time by up to half. Differences between current density predictions in each model were quantified by using a relative difference measure (RDM). Our simulation results showed that [Formula: see text] was the least affected (a maximum of 10% error) for head volumes modeled from the apex to the base of the skull (60%-75% volume). SIGNIFICANCE: This finding suggested that the bone could act as a bioelectricity boundary and thus performing FE simulations of tDCS on the human head with models extending beyond the inferior skull may not be necessary in most cases to obtain reasonable precision in current density results.
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Registered trials
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