EEG source reconstruction accuracy and integration of simultaneous EEG-fMRI resting state data

The resting state functional magnetic resonance imaging (fMRI) approach has allowed to investigate the large scale organization of processing systems in the human brain, revealing that it can be viewed as an integrative network of functionally interacting regions. However, to date the neuronal basis of the fluctuations of the fMRI signal at rest are not fully understood, preventing the possibility to elucidate their functional role. In this scenario, the integration with information derived from electroencephalography (EEG) is very useful, since conversely from fMRI, EEG represents a direct measure of neuronal activity. EEG-fMRI resting state studies investigating the correlation between fMRI signals and corresponding global EEG spectral characteristics in single spectral bands have provided a certain degree of inconsistency in the results. This may be due to the fact that the distinct functional networks involve more than a single frequency band and therefore analysis of simultaneous EEG/fMRI data should consider the whole frequency spectrum. A couple of studies have been performed in this directions but they either did not investigate how the scalp distribution of the EEG spectral metrics affects the patterns of correlations between EEG spectral dynamics and fMRI-derived resting state network or did not identify the specific scalp regions that specifically determined the pattern of observed results. To overcome this gap, with the aim to identify specific spatio-spectral fingerprints of distinct networks, a first study was conducted using an analytical approach that allows to take into account the interplay between the different EEG frequency bands and the corresponding topographic distribution within each network. Specifically, this approach was applied to four sub-components of the Default Mode Network (DMN). Results revealed for the first time the presence of distinctive subcomponent-specific spatial-frequency patterns of correlation between the fMRI signal and EEG rhythm. It should however be noted that spatial resolution of the EEG signal is too low to reliably infer about the location of the involved EEG sources. Therefore, a further step forward could be to try extending the findings of the first study in this direction by performing a source estimation study. Since it is not clear whether the 64 channels EEG system employed in the first study can provide adequate localization performance as regard our regions of interest, an investigation of the source reconstruction accuracy throughout the brain was performed in a second study. Specifically, the 64-channel montage was compared to 32-channel montage, the standard in the clinical practice, as well as to 128-channel montage and to 256- channel montage, considered as the upper reference point. Unlike previous studies, source performances were evaluated all over the cortical grey matter. Results indicate that the localization of the cortical sources of the spatio-spectral fingerprints revealed by the previous study can be adequately inferred by using 64 channels, but a confirmation study with a 128, or even better 256, channels montage is needed. Moreover, particular attention should be paid to investigate deep regions, where localization performance is worse regardless the number of electrodes used.