Neuroelectromagnetic forward head modeling toolbox

Abstract

This paper introduces a Neuroelectromagnetic Forward Head Modeling Toolbox (NFT) running under MATLAB (The Mathworks, Inc.) for generating realistic head models from available data (MRI and/or electrode locations) and for computing numerical solutions for the forward problem of electromagnetic source imaging. The NFT includes tools for segmenting scalp, skull, cerebrospinal fluid (CSF) and brain tissues from T1-weighted magnetic resonance (MR) images. The Boundary Element Method (BEM) is used for the numerical solution of the forward problem. After extracting segmented tissue volumes, surface BEM meshes can be generated. When a subject MR image is not available, a template head model can be warped to measured electrode locations to obtain an individualized head model. Toolbox functions may be called either from a graphic user interface compatible with EEGLAB (http://sccn.ucsd.edu/eeglab), or from the MATLAB command line. Function help messages and a user tutorial are included. The toolbox is freely available under the GNU Public License for noncommercial use and open source development.

Figure 1

The segmentation algorithm used in NFT. Scalp, brain, outer skull, and inner skull are segmented using filtering, thresholding, region growing, morphologic operations, and watershed segmentation.

Figure 2

The nasion and left and right preauricular points shown on an MNI head model.

Figure 3

The NFT main user interface. This window is divided into three panels. The top panel is used to select the working folder and to name the subject and session. The lower panel is the head modeling panel. The lowest panel in the main menu of NFT cues forward model generation.

Figure 4

The NFT head tissue segmentation interface. When a T1-weighted MR image is loaded, anisotropic filtering, scalp, brain, outer skull, and inner skull segmentation are performed.

Figure 5

Segmentation results showing (a) scalp, (b) skull, (c) CSF and (d) brain volumes computed from four subject MR head images acquired using a 3-Tesla GE scanner.

Figure 6

The NFT mesh generation user interface. The mesh generation module uses the results of the segmentation and outputs either three-layer or four-layer BEM head meshes.

Figure 7

BEM models of the scalp, skull, csf and the brain for four subjects: (a) scalp mesh, (b) skull mesh, (c) CSF mesh, (d) brain mesh generated for the volumes shown in Figure 5.

Figure 8

The NFT user interface for warping a template head model to measured 3-D electrode locations.

Figure 9

Three views of the nodes of the template mesh (blue) and the warped mesh (red). The warped mesh fits the electrode locations.

Figure 10

The NFT BEM construction user interface has four panels: (Upper panels) Load a mesh, Load or generate BEM meshes, Load or generate transfer matrices. (Lower panel) Predict scalp potentials produced by given dipole(s).

Figure 11

Scalp projections (two left columns) and equivalent dipole source locations (right column) of two independent components extracted from a 140-electrode EEG recording by infomax ICA. Top row: Scalp maps and equivalent dipole positions computed using the individual subject MR-based BEM head models, (green dipoles) four-layer and (yellow dipoles) three-layer. Middle row: Scalp maps and equivalent dipole positions in the electrode position-warped standard three-layer MNI head model. Bottom row: Scalp maps and equivalent dipole positions based on electrode positions warped to the standard three-layer MNI head model. Slices shown are nearest to the (left posterior) equivalent dipole positions; their computed Talaraich locations are shown (green/yellow lettering).