SimNIBS 4.0: Detailed Head Modeling for Transcranial Brain Stimulation and EEG

Presented During:

Poster No:


Submission Type:

Abstract Submission 


Oula Puonti1, Guilherme Saturnino1, Kristoffer Madsen1, Axel Thielscher2


1Danish Research Centre for Magnetic Resonance, Hvidovre, Copenhagen, 2Copenhagen University Hospital Hvidovre, Copenhagen, Denmark

First Author:

Oula Puonti  
Danish Research Centre for Magnetic Resonance
Hvidovre, Copenhagen


Guilherme Saturnino  
Danish Research Centre for Magnetic Resonance
Hvidovre, Copenhagen
Kristoffer Madsen  
Danish Research Centre for Magnetic Resonance
Hvidovre, Copenhagen
Axel Thielscher  
Copenhagen University Hospital Hvidovre
Copenhagen, Denmark


Computational modeling of the electric currents in the cortex is an integral part of many brain mapping approaches [1,2]. The currents can be either externally induced by electric (TES) or magnetic (TMS) stimulation, or due to neuronal activity in which case they can be measured using M/EEG. In both cases the current flow is largely shaped by the individual anatomy [3], which implies that reliable stimulation targeting, or source reconstructions, require accurate anatomical models of the head anatomy. In the new version of the open-source toolbox for Simulation of Non-Invasive Brain Stimulation (SimNIBS 4.0), we have improved the accuracy and robustness of the anatomical modeling, and included multiple additional head tissue classes, such as veins and spongy bone. SimNIBS 4.0 also supports lead-field calculations, which allows the improved head modeling to be integrated into EEG source reconstruction algorithms.


SimNIBS 4.0 introduces a new approach for automated head tissue segmentation named Complete Head Anatomy Reconstruction Method (CHARM), which automatically segments fifteen different head tissues from, possibly multi-contrast, MRI data (see Figure 1A for an example). CHARM combines a mesh-based probabilistic atlas for modeling the anatomy with a Gaussian Mixture Model (GMM) for modeling the tissue intensities [4]. The mesh node positions and GMM parameters are automatically estimated from the input scan(s), making the approach adaptive to MRI scans acquired with different scanners or using different sequences.

Given the tissue segmentation, a Finite Element Mesh (FEM) is created directly from the volume segmentation using the "Mesh_3" package from CGAL 5.0 [5], which makes the meshing procedure in SimNIBS 4.0 very robust and adaptable. Here, we used the following meshing parameters: facet angle = 30°, facet size = 6mm, facet distance = 0.5 mm, cell radius = 5 mm, and cell size = 5mm. To reduce staircasing effects, the segmentations were initially upsampled to twice the original resolution. Additionally, SimNIBS 4.0 can quickly calculate realistic EEG leadfields by using the reciprocity method [6] and the MKL PARDISO direct solver.


To demonstrate how the new fifteen-tissue head models affect electric field simulations, we use a typical transcranial direct current stimulation (tDCS) montage for targeting the left motor cortex, where the anode is placed over C3 and the cathode electrode is placed in a supraorbital position. Figure 1B shows the current density field around the frontal sinus close to the cathode, which is often labeled as bone when using segmentation approaches implemented in previous versions of SimNIBS [3]. Due to the conductivity differences between bone, which has a low conductivity, and air, which is an insulator, the electric currents flow completely around the frontal sinus creating higher current densities in the gray matter superior to the sinus. This hotspot would likely be missing if the frontal sinus was modeled as bone, as the current pathway would be less distorted.


SimNIBS 4.0 is a free and open-source software for individualized simulation of the electric fields induced by non-invasive brain stimulation and for leadfield calculations in EEG. SimNIBS 4.0 segments multiple head tissues accurately and robustly, and creates finite element meshes for both NIBS simulations and EEG leadfields. In addition, SimNIBS 4.0 supports all the SimNIBS 3 simulation and post-processing features such as TES and TMS electric field simulations, transformations to MNI and FsAverage spaces, and automated electrode montage and coil position optimization.

This work was partially supported by Lundbeckfonden (grant R118-A11308), and NovoNordisk fonden (grant NNF14OC0011413). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 731827.

Brain Stimulation:

Non-invasive Electrical/tDCS/tACS/tRNS 2
Non-invasive Magnetic/TMS

Modeling and Analysis Methods:

Methods Development 1


Computational Neuroscience
Electroencephaolography (EEG)
Transcranial Magnetic Stimulation (TMS)

1|2Indicates the priority used for review
Supporting Image: Fig2_cropped.png
   ·A) Left, the T1w MRI scan of an example subject, and right the corresponding segmentation to fifteen tissue classes. B) Left, the FEM head mesh and right the current density around the frontal sinus.

My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.


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Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

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Structural MRI
Computational modeling

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Provide references using author date format

[1] Thielscher A, Antunes A and Saturnino GB (2015) 'Field modeling for transcranial magnetic stimulation: A useful tool to understand the physiological effects of TMS?' 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE) pp. 222–5
[2] Cho JH, Vorwerk J, Wolters CH and Knösche TR (2015) 'Influence of the head model on EEG and MEG source connectivity analyses' Neuroimage, vol 110, pp. 60-77
[3] Nielsen JD, Madsen KH, Puonti O, Siebner HR, Bauer C, Madsen CG, Saturnino GB and Thielscher A (2018) 'Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art', Neuroimage, vol 174, pp. 587–98
[4] Puonti O, Iglesias JE and Van Leemput K (2016) 'Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling', Neuroimage, vol 143, pp. 235-249
[5] Alliez P, Jamin C, Rineau L, Tayeb S, Tournois J and Yvinec M (2019) '3D Mesh Generation CGAL User and Reference Manual', (CGAL Editorial Board)
[6] Rush S and Driscoll D (1969) 'EEG electrode sensitivity--an application of reciprocity', IEEE Trans. Biomed. Eng., vol 16, pp. 15–22