A Comparison of Brain Metabolic Connectivity Methods Topology, Cognition, Age, Structure, and Genetics

Brain metabolic connectivity is increasingly studied using positron emission tomography (PET), but multiple analytical approaches exist with unknown comparability. Here we compared metabolic connectivity approaches in 80 participants who underwent simultaneous dynamic PET with 18F-fluorodeoxyglucose and functional magnetic resonance imaging (fMRI) scans and cognitive testing. Six connectivity approaches were compared, including the traditional across-subject method (often labelled metabolic covariance) and five approaches that estimate individual-level connectivity: across-subject metabolic covariance with individual scores derived using leave one out (LOO); functional PET (fPET) within-subject correlations of regional timeseries; and three approaches that use distance-based measures to estimate individual-level connectivity based on within-region tracer distribution and kinetic compartment C1 and C2 time activity curves. fMRI connectivity was also calculated. Each method showed distinct topological, behavioural, structural and genetic signatures. fPET connectivity demonstrated the strongest cognitive associations and largest age-related declines, with unique predictive power for cognition independent of age. Tracer distribution showed strong age group discrimination although with mixed direction of age and cognitive effects. Metabolic covariance (LOO) and kinetic C2 showed no significant age group differences and their cognition associations reflected age-related variance. Gene expression associations implicated a shared core glycolytic-insulin machinery in all approaches, but the direction of the association with regional connectivity diverged across approaches and unique gene signatures further distinguished most methods. Collectively, these results indicate that the optimal connectivity approach depends on the research question. fPET connectivity with dynamic scans offers utility for cognition and ageing studies, tracer distribution for single-scan designs with careful interpretation of age and cognition relationships, kinetic approaches for novel biological insights into glucose kinetics and fMRI for multi-modal integration. Metabolic covariance (LOO) should be used with caution for brain-behaviour investigations. Researchers should weigh trade-offs between physiological specificity, analytical complexity, acquisition requirements and interpretability when selecting a metabolic connectivity approach.