Abstract
Beamformers are a widely-used tool in brain analysis with magnetoencephalography (MEG) and electroencephalography (EEG). For the construction of the beamformer filters realistic head volume conductor modeling is necessary for accurately computing the EEG and MEG leadfields, i.e., for solving the EEG and MEG forward problem. In this work, we investigate the influence of including realistic head tissue compartments into a finite element method (FEM) model on the beamformer's localization ability. Specifically, we investigate the effect of including cerebrospinal fluid, gray matter, and white matter distinction, as well as segmenting the skull bone into compacta and spongiosa, and modeling white matter anisotropy. We simulate an interictal epileptic measurement with white sensor noise. Beamformer filters are constructed with unit gain, unit array gain, and unit noise gain constraint. Beamformer source positions are determined by evaluating power and excess sample kurtosis (g2) of the source-waveforms at all source space nodes. For both modalities, we see a strong effect of modeling the cerebrospinal fluid and white and gray matter. Depending on the source position, both effects can each be in the magnitude of centimeters, rendering their modeling necessary for successful localization. Precise skull modeling mainly effected the EEG up to a few millimeters, while both modalities could profit from modeling white matter anisotropy to a smaller extent of 5–10 mm. The unit noise gain or neural activity index beamformer behaves similarly to the array gain beamformer when noise strength is sufficiently high. Variance localization seems more robust against modeling errors than kurtosis.
Highlights
Magnetoencephalography (MEG) and electroencephalography (EEG) are non-invasive functional brain mapping tools with very high temporal resolution (Brette and Destexhe, 2012) and, in case of sufficiently realistic volume conductor modeling, appropriate spatial resolution
We find that a simple normalization of the leadfield by its Frobenius norm is not enough to reliably localize activity in noisy data, while the commonly used neural activity index and the array gain beamformer by Sekihara and Nagarajan (2008) work well for both the localization with variance and kurtosis
We find kurtosis to be less robust to modeling errors, making good head modeling especially necessary for epilepsy source reconstruction
Summary
Magnetoencephalography (MEG) and electroencephalography (EEG) are non-invasive functional brain mapping tools with very high temporal resolution (Brette and Destexhe, 2012) and, in case of sufficiently realistic volume conductor modeling, appropriate spatial resolution. They are useful to study highly dynamic neural activity. The solution of the EEG and MEG inverse problem is relying on the solution of the forward problem, i.e., the simulation of EEG and MEG for a given source in the brain. For the solution of the forward problem, the geometrical and electromagnetic. Diffusion tensor imaging (DTI) is used to gain anisotropy information (Tuch et al, 2001; Güllmar et al, 2010; Vorwerk et al, 2014)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
