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Effective connectivity metrics are further evaluated for each pair of ROIs through the pairwise implementation of the time domain Granger Causality (Granger, 1969). From the estimated effective connectivity metrics for each pair of ROIs, an effective connectivity matrix (e-CM) is calculated. PET preprocessing The original PET data is first converted from the original format to NIFTI 4D. The converted PET data is further corrected for motion using SPM and smoothed with a 8 mm Gaussian filter. The dynamic PET data is then summed into a NIFTI 3D image and non-linearly registered to the aMRI. The aligned parcellated ROIs are then mapped back to the PET space through the inverse transformation. For each ROI the dynamic PET series are extracted and its mean value per ROI is calculated. The Pearson correlation is PI3K inhibitor then used to generate a correlation matrix between each pair of ROIs (PET connectivity matrix). Further, for the summed image, the mean standard uptake values (SUV) are calculated for each ROI. Group connectivity and graph theory analysis For each subject, a hybrid structural + functional connectivity matrix (sf-CM) is calculated resulting from the multiplication of s-CM and f-CM matrices (if raw data of both modalities are provided). Further, binary s-CM (number of fibers > 0), f-CM (p-values Bosutinib ic50 robustness connectivity (robustness-CM) matrices are computed. The mean-CM result from averaging technique/modality or hybrid connectivity weighted matrices translating information regarding the strength of connections (number of fibers in dMRI, or correlation coefficient in fMRI). The robustness-CM results from the mean of the binary matrices, providing a measure of the robustness of each connection (e.g., a value Dabigatran of 0.1 in the robustness-CM states that only 10% of the subjects show connections between a certain ROI pair, while a value of 0.9 states that those connections are present in 90% of the subjects). Group s-CM, f-CM and sf-CM are further evaluated regarding general graph-theory metrics, namely mean network degree, mean clustering coefficient, characteristic path length and small-worldness calculated using the Brain Connectivity Toolbox (BCT: https://sites.google.com/site/bctnet/ (Rubinov & Sporns, 2010)). Additionally, normalized indexes based on these general metrics were calculated for an easier comparison between the different graphs. These normalized indexes were then calculated by the ratio of the metrics and their variant obtained from 10 random graphs, which were generated by shuffling the data-driven graph while maintaining symmetry and mean degree. Individual ROI graph theory metrics, such as node degree and clustering coefficient, were also calculated using the BCT toolbox and saved for further analysis.