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

The brains 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 networks 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 regions signaling dominance that integrates connectivity 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.