To assess functional connectivity of brain networks fMRI methods are often applied, whereas positron emission tomography (PET) can reveal metabolic changes in the brain, and metabolic brain connectivity (Cometomics). Medium time scales (seconds) are accessible using fMRI methods, whereas slower time scales can ideally be studied using PET. The combination of simultaneous PET/MR with high temporal resolution electroencephalogram (EEG) allows a detailed tracing of neuronal processing over a wide temporal range. Aim of this study was to analyze prominent resting state networks such as the default mode network using a novel simultaneous PET/MR/EEG imaging approach.Patients suffering from epilepsy (n=11, informed consent) were measured using simultaneous PET/MR/EEG at 3 T field strength. The influence of the EEG cap on PET data was analyzed, by comparison with scans without EEG cap. In combination, with simultaneous EEG recordings (256 channels, sampling rate 1000 Hz), BOLD-fMRI (EPI, TR=2500 ms, TE=32ms, Matrix Size: 646436, Voxel Size: 3.53.53.5 mm3) and [18F] fluorodeoxyglucose (FDG, a tracer for glucose metabolism)-PET (injected dose 180-200 MBq) imaging was performed during resting state. PET acquisition time was 60 min. The first 20 min of BOLD-fMRI and EEG were compared with the first 20 min of PET data. PET images were reconstructed into 60 s frames (OSEM reconstruction). Preprocessing was performed using SPM 12. Independent component analysis (ICA) for PET and MR data were performed to visualize RSNs. Region of interest analysis (ROI-analysis) in PET, MR and EEG data was used to investigate Pearson´s correlation coefficient within RSN. Based on the AAL atlas (116 ROIs), the whole brain PET/MR data was analyzed. A special focus was on areas that belong to the default mode network (DMN), where correlation graphs were computed for PET and simultaneously acquired BOLD-fMRI. In addition, preliminary EEG data of four patients (one 20 s epoch per patient acquired during the simultaneous PET/MR scan) were evaluated to investigate correlation coefficients within DMN.The influence of EEG cap on the PET data was in the range of approximately -5 % in cortical regions. ICA PET and MR data revealed similarities as well as discrepancies of imaged RSNs e.g. DMN, visual and auditory. Correlation matrices of both modalities representing the whole brain, indicate disparities between both modalities (Figure 1). In regard to the DMN, strong correlation can be observed in PET data (Figure 2). With increased temporal resolution of modalities the correlation within DMN is decreased in MR and even more decreased in the mean EEG data of 4 patients for the DMN regions (Figure 2). However, we observed a large variety of the individual DMN correlation matrices for four patients analyzed. Some patients clearly showed DMN like-structures already visible in 20 s epochs of data.Including additional information (e.g. from CT) of the EEG cap into the attenuation correction process can further reduce the effect of the EEG cap on the PET data. Our study in general revealed that RSNs in the brain can be studied using both PET and fMRI information. The PET networks show to some respect resemblance to the RSNs found in fMRI. However, in many instances also differences e.g. in form of higher and lower correlation coefficients between certain brain regions exist. These discrepancies can most likely be attributed to the different physiological origins of the studied signals (PET: metabolic, fMRI: hemodynamic). The DMN showed differences in correlations between regions when studied in PET, fMRI and EEG. Our initial analysis indicated that PET showed even highest correlations, whereas the average EEG showed lowest. Notably, there was a large variance in the initial four EEG patients analyzed, which could stem from a variety of reasons including different disease manifestations as well as onset times of the selected EEG epochs.In conclusion our results point towards a multiscalar temporal manifestation of certain brain networks, which can be ideally studied using simultaneous PET/MR/EEG. This work opens the domain for further studies in healthy subjects as well as clinical cases to decode the enigmatic nature of brain network structure.