Investigation of tDCS volume conduction effects in a highly realistic head model

Wagner, Sven; Rampersad, Sumientra; Aydın, Ümit; Vorwerk, Johannes; Oostendorp, Thom F.; Neuling, Toralf; Herrmann, Christoph S.; Stegeman, Dick F.; Wolters, Carsten H.

doi:10.1088/1741-2560/11/1/016002

Cited by 164 publications

(192 citation statements)

References 52 publications

Supporting

Mentioning

178

Contrasting

Order By: Relevance

“…By extending FOV with a standard head to capture the anatomy down to the neck we demonstrated that capturing current flows through the entire head significantly improved the modeling results. While most studies argue that the CSF and fluid-filled ventricles should be included in models Wagner et al, 2014;Opitz et al, 2015), others have argued that conventional 1 mm 3 MRI resolution cannot measure CSF space accurately and thus, it can be omitted (Im et al, 2008;Park et al, 2011;Jung et al, 2013). Our results indicate that including CSF into the model is important for correctly predicting the magnitude of achieved electric field in the brain.…”

Section: Guidelines For Modelingmentioning

confidence: 76%

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

et al. 2017

View full text Add to dashboard Cite

Transcranial electric stimulation aims to stimulate the brain by applying weak electrical currents at the scalp. However, the magnitude and spatial distribution of electric fields in the human brain are unknown. We measured electric potentials intracranially in ten epilepsy patients and estimated electric fields across the entire brain by leveraging calibrated current-flow models. When stimulating at 2 mA, cortical electric fields reach 0.8 V/m, the lower limit of effectiveness in animal studies. When individual whole-head anatomy is considered, the predicted electric field magnitudes correlate with the recorded values in cortical (r = 0.86) and depth (r = 0.88) electrodes. Accurate models require adjustment of tissue conductivity values reported in the literature, but accuracy is not improved when incorporating white matter anisotropy or different skull compartments. This is the first study to validate and calibrate current-flow models with in vivo intracranial recordings in humans, providing a solid foundation to target stimulation and interpret clinical trials.

show abstract

Section: Guidelines For Modelingmentioning

confidence: 76%

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Two recent simulation studies have extended simple three-compartment head models to complex six-compartment models step-by-step to study the effects of modeling various aspects of the human head [8,27]. Both studies also included anisotropic conductivity information derived from diffusion tensors.…”

Section: Discussionmentioning

confidence: 99%

“…following transcranial direct current stimulation) along trajectories parallel to the primary direction of the fiber tracts [4,27]. Sources located deep in the brain, as well as those bordering strongly anisotropic tissue, are misrepresented in EEG localization when neglecting the effects of white matter anisotropy [4].…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, our FE mesh is of higher quality than many previous studies, as we use an unstructured tetrahedral mesh, rather than structured hexahedral grids (used in e.g. [27,6]). This allows us to refine the mesh for more realistic calculations within highly curved regions of the brain.…”

Section: Discussionmentioning

confidence: 99%

“…SimBio also relies on many outdated file formats which makes interoperability difficult. There is also no standard or reproducible meshing procedure available in SimBio; in recent publications skin and skull meshing has been performed, at least in part, using commercial software [8,27].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A finite-element reciprocity solution for EEG forward modeling with realistic individual head models

et al. 2014

View full text Add to dashboard Cite

We present a finite element modeling (FEM) implementation for solving the forward problem in electroencephalography (EEG). The solution is based on Helmholtz's principle of reciprocity which allows for dramatically reduced computational time when constructing the leadfield matrix. The approach was validated using a 4-shell spherical model and shown to perform comparably with two current state-of-the-art alternatives (OpenMEEG for boundary element modeling and SimBio for finite element modeling).We applied the method to real human brain MRI data and created a model with five tissue types: white matter, grey matter, cerebrospinal fluid, skull, and scalp. By calculating conductivity tensors from diffusion-weighted MR images, we also demonstrate one of the main benefits of FEM: the ability to include anisotropic conductivities within the head model. Root-mean square deviation between the standard leadfield and the leadfield including white-matter anisotropy showed that ignoring the directional conductivity of white matter fiber tracts leads to orientation-specific errors in the forward model. Realistic head models are necessary for precise source localization in individuals. Our approach is fast, accurate, open-source and freely available online.

show abstract

Detecting large-scale networks in the human brain using high-density electroencephalography

et al. 2017

View full text Add to dashboard Cite

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc.

show abstract

Investigation of tDCS volume conduction effects in a highly realistic head model

Cited by 164 publications

References 52 publications

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation

A finite-element reciprocity solution for EEG forward modeling with realistic individual head models

Detecting large-scale networks in the human brain using high-density electroencephalography

Contact Info

Product

Resources

About