Abstract
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 min [18F]FDG positron emission tomography (PET) images of 67 healthy subjects between 20 and 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 (KLSE). 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) was assessed within the whole brain and within predefined functional subnetworks. Overall, a decrease of metabolic connectivity strength with healthy aging 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 aging, while the distribution and the number of hubs did not change with aging, 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 aging, 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.
Highlights
[18F]FDG positron emission tomography (PET) is a valuable molecular neuroimaging technique to study the glucose metabolism in the human brain which in turn serves as a proxy for neuronal activity
These studies showed increased gray matter (GM) atrophy observed by voxel-based morphometry MRI analyses (Good et al, 2001; Allen et al, 2005; Smith et al, 2007; Bagarinao et al, 2018) and reduced cerebral structural integrity assessed by diffusion tensor imaging (DTI) (Moseley, 2002; Head et al, 2004; Sullivan and Pfefferbaum, 2006) and differences in brain activation patterns using functional MRI (Grady, 2012; Avelar-Pereira et al, 2017) due to aging
In contrast to structural and functional connectivity, brain metabolic connectivity findings using [18F]FDG PET are mainly based on group-level analyses (Sala and Perani, 2019) where correlations between regional uptake values across subjects are used as connectivity measures between different brain regions
Summary
[18F]FDG positron emission tomography (PET) is a valuable molecular neuroimaging technique to study the glucose metabolism in the human brain which in turn serves as a proxy for neuronal activity. The aim of this study was to apply this novel technique on [18F]FDG PET-MR data of a cohort of 67 healthy controls, covering an age range of 20–82 years, to evaluate age-related effects on graph-based connectivity measures for network integration, segregation, and centrality. We evaluated these age effects on the level of both the whole brain and different functional brain networks where we considered networks which represent the intrinsic functional connectivity of the cerebral cortex. This functional atlas includes the frontoparietal (4 VOIs) network together with seven functional subnetworks containing the visual (13 VOIs), somatomotor (14 VOIs), dorsal attention (13 VOIs), salience and ventral attention (14 VOIs), limbic (5 VOIs), control (16 VOIs), and default mode (21 VOIs) network (Supplementary Table 1)
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