Quantitative evaluation in estimating sources underlying brain oscillations using current source density methods and beamformer approaches

Abstract

Brain oscillations from electro-encephalogram (EEG) and magneto-encephalogram (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 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 ratios while detecting already known location of neural activity generators under varying signal to noise ratios. Second, empirical EEG data during auditory steady state responses (ASSR) 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 false positives are ignored. Interestingly, focal activation map is generated by LCMV and DICS when compared to eLORETA while control of false positives 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.

Significance statement State-of-the-art methods of source localization techniques, e.g., current density methods, minimum norm estimates and beamformers report distributed brain activity patterns that are often not in consensus for a putative task. This article offers ground truth validation of these techniques in the context of different kind of source detections, e.g, determining the sources underlying key events (evoked potentials) and steady state brain oscillations (band limited brain activity). The broader goal is to help cognitive neuroscientists select the most effective source localization technique that is in sync with the signal processing needs required for targeting a specific question.