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 such as exact low resolution electromagnetic tomography (eLORETA), 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 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. In this technical note, 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 centroid error, focal width, reciever operating characteristics (ROC) 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. Inportantly, we found a less-distributed activation map is generated in LCMV and DICS when compared to eLORETA however, control of false positivesis much superior in eLORETA in realistic distributed dipole scenarios. A comprehensive analysis, further, indicate the strengths and drawbacks of each of the methods.

Decreased Resting State Inter- and Intra-Network Functional Connectivity Is Associated with Perceived Stress in a Sample of University Students: An eLORETA Study

“…Like other methods, eLORETA is characterized by strengths and weaknesses [55]. Despite this, it has been reported that compared to other solutions (e.g., minimum norm estimates), the probability of incorrectly detecting an activation is lower for the eLORETA [55]. Furthermore, compared to older versions (e.g., sLORETA), this new update performs better concerning correct localization and visualization, suppressing nonsignificant details in the following reconstruction [53].…”

“…All EEG data were analyzed through the exact low-resolution brain electromagnetic tomography software (eLORETA [52]), a validated device for the investigation of neural networks functional connectivity [32]. The eLORETA, the advanced version of sLORETA, is considered one of the most widely adopted methods among the brain source localization procedures [53][54][55].…”

“…Like other methods, eLORETA is characterized by strengths and weaknesses [55]. Despite this, it has been reported that compared to other solutions (e.g., minimum norm estimates), the probability of incorrectly detecting an activation is lower for the eLORETA [55].…”

Probing the “Default Network Interference Hypothesis” With EEG: An RDoC Approach Focused on Attention

“…Similar conclusions can be found in another recent, more realistic, independent study. 38 Some recent reports providing direct experimental validation for eLORETA can be found, for instance, in Montani et al 39 where eLORETA localizes correctly well-known language-related areas, and in Göschl et al 40 and Buchholz et al 41 where eLORETA correctly localizes somatosensory-motor areas.…”

A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertainties

“…The localized brain source resulting from this so-called EEG inverse problem then can be used for a better diagnosis and treatment of mental or neurological disorders such as epilepsy [3], depression [4], and schizophrenia [5]. eLORETA is one of the most accurate methods in localizing a single focal source [6] in comparison with other distributed source estimation techniques such as minimum norm estimation (MNE) [7], weighted MNE (WMNE) [7], lowresolution electromagnetic tomography (LORETA) [8], and standardized LORETA (sLORETA) [9]. Furthermore, eLORETA shows a better performance in suppressing less significant sources and produces less blurred results in comparison with its predecessor the sLORETA technique [9], [10].…”