Quantitative Evaluation in Estimating Sources Underlying Brain Oscillations Using Current Source Density Methods and Beamformer Approaches

Abstract

Brain oscillations from EEG and MEG shed light on neurophysiological mechanisms of human behavior. However, to extract information on cortical processing, researchers have to rely on source localization methods that can be very broadly classified into current density estimates such as exact low-resolution brain electromagnetic tomography (eLORETA), minimum norm estimates (MNE), and beamformers such as dynamic imaging of coherent sources (DICS) and linearly constrained minimum variance (LCMV). These algorithms produce a distributed map of brain activity underlying sustained and transient responses during neuroimaging studies of behavior. On the other hand, there are very few comparative analyses that evaluates the “ground truth detection” capabilities of these methods. The current article evaluates the reliability in estimation of sources of spectral event generators in the cortex using a two-pronged approach. First, simulated EEG data with point dipoles and distributed dipoles are used to validate the accuracy and sensitivity of each one of these methods of source localization. The abilities of the techniques were tested by comparing the localization error, focal width, false positive (FP) ratios while detecting already known location of neural activity generators under varying signal-to-noise ratios (SNRs). Second, empirical EEG data during auditory steady state responses (ASSRs) in human participants were used to compare the distributed nature of source localization. All methods were successful in recovery of point sources in favorable signal to noise scenarios and could achieve high hit rates if FPs are ignored. Interestingly, focal activation map is generated by LCMV and DICS when compared to eLORETA while control of FPs is much superior in eLORETA. Subsequently drawbacks and strengths of each method are highlighted with a detailed discussion on how to choose a technique based on empirical requirements.

Cortical source imaging of resting-state MEG with a high resolution atlas: An evaluation of methods

“…However, choosing MNE/wMNE comes at the cost of localization errors (i.e., non-zero LE and fLE), a reduced robustness to sensor noise compared to eLORETA, and reduced ability to explain sensor-level data. These results are in line with those of dipole simulations, in which eLORETA largely outperformed MNE with the exception of a measure of leakage ('focal width') (Halder et al, 2019). It should additionally be noted that MNE/wMNE performed poorly on parcellated data (i.e., low r 2 CV , Figure 7).…”

“…MNE invariably explained 100% of the input data, but was unable to predict data from a left-out sensor in the cross validation procedure, suggesting an overfitting to the data that is not necessarily representative of true underlying source dynamics. LCMV exhibited high LE in line with studies using simulated dipoles (Pascual-Marqui et al, 2018;Halder et al, 2019), and was consistently outperformed by its weighted counterpart. Therefore for resting-state MEG source reconstruction, wLCMV is preferred over LCMV.…”

“…Another crucial methodological decision was choice of methods used to compare different algorithms. Previous studies have compared algorithms for source localization -identifying the origin of a small number of sources (Bai et al, 2007;Hassan et al, 2014;Bradley et al, 2016;Finger et al, 2016;Barzegaran and Knyazeva, 2017;Hassan et al, 2017;Hincapié et al, 2017;Bonaiuto et al, 2018;Pascual-Marqui et al, 2018;Seeland et al, 2018;Anzolin et al, 2019;Halder et al, 2019), such as known networks during task or simulated dipoles. These methods are not directly generalizable to resting-state data, where activity is not a point source but is distributed widely across the cortex.…”

“…Care must be taken when interpreting the LE in the resting-state paradigm. We are not attempting to quantify the difference in peak activation of the estimate given a single 'true' active dipole as in simulation or theoretical studies (Pascual-Marqui, 2007;Barzegaran and Knyazeva, 2017;Pascual-Marqui et al, 2018;Halder et al, 2019), because of the expected cross talk from distributed sources in resting-state data. As a result, 'true' activity at a certain location does not necessarily imply a peak of estimated activity at that location -for example, localization statistics based on peak activation for sLORETA are accurate for single dipole localization, but are imperfect for localizing multiple dipoles (Bradley et al, 2016).…”

“…Much effort has been made to assess the quality of source reconstruction algorithms in the literature, which mainly focuses on source localization, i.e. identifying the origin of a small number of sources, for example evoked potentials with ground truth based on known task-relevant activity (Bai et al, 2007;Hassan et al, 2014;Seeland et al, 2018;Halder et al, 2019), or simulated activity at a small number of dipoles (Bradley et al, 2016;Finger et al, 2016;Barzegaran and Knyazeva, 2017;Hassan et al, 2017;Hincapié et al, 2017;Bonaiuto et al, 2018;Pascual-Marqui et al, 2018;Anzolin et al, 2019;Halder et al, 2019).…”