Bayesian model averaging in EEG/MEG imaging

A pre-task resting condition neither 'baseline' nor 'zero'

This study introduce the comparison of a 'reference state' versus a resting condition (zero), defined upon normative developmental equations. We compared pre-task 'resting' data from healthy individuals with the Normative Cuban digital resting EEG-database recently calculated for VARETA (qEEG/VARETA). The results allowed us to state that a 'pre-task resting conditions' exists as a state beyond the 'zero' or 'baseline' condition. The pre-task 'resting' condition is never truly 'at rest', however most of the previous published fMRI/PET studies assumed such a pre-task condition as reference/baseline condition. By defining different 'resting states' by qEEG/VARETA analyses, we have a potential methodology which can define resting state conditions and to be sure that they are consistent when comparing within group analyses across tasks or between groups either void of task or for task specific conditions.

EEG source imaging

OBJECTIVE

Electroencephalography (EEG) is an important tool for studying the temporal dynamics of the human brain's large-scale neuronal circuits. However, most EEG applications fail to capitalize on all of the data's available information, particularly that concerning the location of active sources in the brain. Localizing the sources of a given scalp measurement is only achieved by solving the so-called inverse problem. By introducing reasonable a priori constraints, the inverse problem can be solved and the most probable sources in the brain at every moment in time can be accurately localized.

METHODS AND RESULTS

Here, we review the different EEG source localization procedures applied during the last two decades. Additionally, we detail the importance of those procedures preceding and following source estimation that are intimately linked to a successful, reliable result. We discuss (1) the number and positioning of electrodes, (2) the varieties of inverse solution models and algorithms, (3) the integration of EEG source estimations with MRI data, (4) the integration of time and frequency in source imaging, and (5) the statistical analysis of inverse solution results.

CONCLUSIONS AND SIGNIFICANCE

We show that modern EEG source imaging simultaneously details the temporal and spatial dimensions of brain activity, making it an important and affordable tool to study the properties of cerebral, neural networks in cognitive and clinical neurosciences.