Metabolism-weighted brain connectome reveals synaptic integration and vulnerability to neurodegeneration

ABSTRACT The brain’s capacity for integration arises from both its structural wiring and energetically demanding electrochemical signaling. Yet current connectome analyses treat network nodes as functionally homogeneous, ignoring that neural communication is constrained by metabolic cost. Here we introduce a metabolism-weighted connectome, a fully weighted brain graph in which both connections and the metabolic activity of each node describe the network’s capacity for integration. Using three datasets of simultaneous fMRI and FDG-PET acquisitions, we define metabolism-weighted centrality (MwC), a biologically grounded index of each region’s signaling dominance that integrates functional connectivity (FC) with local energy metabolism. MwC provides a more accurate representation of cortical activity flow than classical edge-based metrics and reveals that metabolically active hubs align with higher-order cognitive networks. Transcriptomic and synaptic imaging data demonstrate that these hubs exhibit increased synaptic energy turnover, linking activity-driven centrality to the molecular architecture of signaling. Notably, the same high-MwC regions show greater susceptibility to neurodegenerative pathology, suggesting that lifelong metabolic demand influences both integrative function and disease vulnerability. By linking neuronal metabolism to network organization, our framework bridges cellular energetics and system-level computation, opening new avenues for interpreting brain vulnerability and performance. Significance Statement The various regions of the brain exhibit unequal energy consumption. Specifically, areas associated with cognitive functions such as memory and attention are metabolic hotspots of intense neural activity. Yet standard connectivity analyses ignore this energetic dimension, treating all regions as functionally equivalent. We introduce the metabolism-weighted connectome (MwC), a framework that maps brain connectivity weighted by each region’s energy expenditure. Using simultaneous PET-MRI in healthy volunteers, we show that energetically most active hubs anchor networks for complex cognition, exhibit molecular signatures of intense synaptic activity, and are disproportionately vulnerable to Alzheimer’s disease. These findings establish a unifying principle: the brain regions most essential for cognition bear the greatest metabolic burden, and this cost may underlie their susceptibility to neurodegeneration.