The Effect of Aging on Brain Glucose Metabolic Connectivity Revealed by [18F]FDG PET-MR and Individual Brain Networks - Mertens, Nathalie; Sunaert, Stefan; Van Laere, Koen; Koole, Michel (Frontiers in Aging Neuroscience, 2022)
Contrary to group-based brain connectivity analyses, the aim of this study was to construct individual brain metabolic networks to determine age-related effects on brain metabolic connectivity. Static 40-60 minutes [18F]FDG PET images of 67 healthy subjects between 20-82 years were acquired with an integrated PET-MR system. Network nodes were defined by brain parcellation using the Schaefer atlas, while connectivity strength between two nodes was determined by comparing the distribution of PET uptake values within each node using a Kullback-Leibler divergence similarity estimation. After constructing individual brain networks, a linear and quadratic regression analysis of metabolic connectivity strengths within- and between networks was performed to model age-dependency. In addition, the age dependency of metrics for network integration (characteristic path length), segregation (clustering coefficient and local efficiency) and centrality (number of hubs) were assessed within the whole brain and within predefined functional subnetworks. Overall, a decrease of metabolic connectivity strength with healthy ageing was found within the whole brain network and several subnetworks except within the somatomotor, limbic and visual network. The same decrease of metabolic connectivity was found between several networks across the whole brain network and the functional subnetworks. In terms of network topology, a less integrated and less segregated network was observed with ageing, while the distribution and the number of hubs did not change with ageing, suggesting that brain metabolic networks are not reorganized during the adult lifespan. In conclusion, using an individual brain metabolic network approach, a decrease in metabolic connectivity strength was observed with healthy ageing, both within the whole brain and within several predefined networks. These findings can be used in a diagnostic setting to differentiate between age-related changes in brain metabolic connectivity strength and changes caused by early development of neurodegeneration.