Performance evaluation of inverse methods for identification and characterization of oscillatory brain sources: Ground truth validation & empirical evidences

Abstract

Oscillatory brain electromagnetic activity is an established tool to study neurophysiological mechanisms of human behavior using electro-encephalogram (EEG) and magneto-encephalogram (MEG) techniques. Often, to extract source level information in the cortex, researchers have to rely on inverse techniques that generate probabilistic estimation of the cortical activation underlying EEG/ MEG data from sensors located outside the body. State of the art source localization methods using current density estimates such as exact low resolution electromagnetic tomography (eLORETA) and minimum norm estimates (MNE) as well as beamformers such as Dynamic Imaging of Coherent Sources (DICS) and Linearly Constrained Minimum Variance (LCMV) have over the years been established as the prominent techniques of choice. However, these algorithms produce a distributed map of brain activity underlying sustained and transient responses during neuroimaging studies of behavior. Furthermore, the volume conduction effects, phase lags between sources and noise of the environment play a considerable role in adding uncertainty to source localization. There are very few comparative analyses that evaluates the \"ground truth detection\" capabilities of these methods and evaluates their efficacies based on sources in temporal cortex relevant for auditory processing as well as mesial temporal lobe epilepsies. In this Methods article, we compare the aforementioned techniques to estimate sources of spectral event generators in the cortex using a two-pronged approach. First, we simulated EEG data with point dipole (single and two-point), as well as, distributed dipole modelling techniques 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 and depths of sources from cortical surface. Secondly, we performed source localization on empricial EEG data collected from human participants while they listened to rhythmic tone stimuli binaurally. Importantly, we found a less-distributed activation map is generated by LCMV and DICS when compared to eLORETA. However, control of false positives is much superior in eLORETA especially while using realistic distributed dipole scenarios. We also highlight the strengths and drawbacks of eLORETA, LCMV and DICS following a comprehensive analysis of simulated and empirical EEG data.