Basic research in resting state networks (RSN) has demonstrated altered connectivity pattern in regards to neuronal diseases and is important for clinical translation. It is known that structural and functional connectivity is changed in clinical populations e.g. Azheimer´s disease, or Epilepsia. Such changes can be investigated by using imaging technologies like MRI or positron emission tomography (PET). In this study we investigated prominent resting state networks in rats assessed by simultaneous PET/MR. The complementary nature between PET and MR could deliver more detailed information about neuronal network organization and communication, not only on a functional but also on a metabolic scale.

Lewis rats (n=4, male, ca 350 g ± 50 g) were measured using a simultaneous PET/MR system, designed for small animals (7 T MR; isolfurane anesthesia). fMRI-BOLD was measured over 20 min to reveal functional connectivity during resting state (EPI, TR=3000 ms, TE 18 ms, Matrix Size 64×64×16, Voxel Size: 0.5×0.5×1 mm³, 400 volumes). Metabolic information was measured by using [¹⁸F]FDG-PET with an acquisition time of 60 min (29.6 MBq; OSEM reconstruction into 60 frames). PET and MR data were preprocessed, using SPM 12. Statistic analyses were performed by using Matlab (2012b) to evaluate Pearson´s correlation coefficient between ROI timeseries; GIFT for independent component analysis (ICA) (40 components, ICASSO algorithm). After ICA, prominent RSNs e.g. DMN were assessed. PET and MR network images were overlaid and compared, using dice coefficients (dc). ROI and the correlation strengths between them were identified by a ROI-analysis using PMOD and Matlab (2012b). Furthermore, distances of areas within networks were compared with their connectivity strength. Additionally, the cerebellum was included to the DMN to investigate complementary information between PET and MR.Prominent RSNs were identified in both modalities, for instance the cingulate cortex (Cg1, Cg2 dc: 0.47, overlap: 68%), motor cortex (M1, dc 0.20, overlap: 22%), caudate putamen (CPu, dc: 40, overlap: 48%). Furthermore, DMN-like structure could be identified with MR (8 ICA components) as well as with PET (7 ICA components). In general, Z-scores from MR data were higher compared to PET. However, also disparities could be observed between both modalities. Therefore, connection strengths within specific networks were investigated (Figure 1, 2). ROI-analysis based on PET data presented additional weak and negative correlation between areas involved in the DMN (Figure 3). In contrast, MR showed only strong positive correlation within the DMN. No influence of the regional distance on connectivity strength could be observed in both modalities (Figure 4). Furthermore, PET suggested a participation of the cerebellum to the DMN in contrast to MR. This study though preliminary in nature, demonstrates that RSN could be imaged not only with MR but also with PET. This novel technique of metabolic connectivity mapping (Cometomics) allows an accurate comparison between PET/MR. The study revealed similarities but also discrepancies between PET/MR images in motor and ganglia networks during resting state. Discrepancies can be explained by the complementary nature (BOLD-fMRI: hemodynamic, [¹⁸F]FDG-PET: metabolic) between modalities that offers a new perspective to combine functional and metabolic connectivity in order to receive a deeper insight into neuronal processing. Although, ICA showed high similarities between PET and MR, ROI-analysis revealed significant differences within RSNs. Therefore, ROI-analysis is suited for a more detailed look at global neuronal processing. Correlation coefficients between network-involved areas were significant higher in MR data than in PET data, probably due to larger amounts of images involved in MR analysis compared to PET. In contrast to PET, MR data indicated only positive connectivity between all areas that are involved in the DMN, whereas PET revealed predominately negative connectivity. In that case PET could indicate that metabolic signals show other neuronal processes than MR e.g. inhibition (possibly negative correlation) vs. excitation (possibly positive correlation) processes. Furthermore, the distance factor between areas within networks didn´t indicates an influence on connectivity strength, which illustrates the stability of such networks. In addition, we included the cerebellum to the DMN. Interestingly, MR data postulated no connection between DMN and cerebellum. However, PET data illustrated negative correlation between DMN and cerebellum. Here, further investigations of PET/MR data are needed.In conclusion, based on simultaneous PET/MR we showed nearly optimal matching between certain PET and MR derived brain networks, using ICA. However, via ROI-analysis, differences within networks became more prominent. This work serves as a catalyzer for further research on the interpretation and evaluation of hemodynamic and metabolic signals ultimately allowing a deeper insight into brain processing.