Metabolism-weighted brain connectome reveals synaptic integration and vulnerability to neurodegeneration - Mahnaz Ashrafi; Laura Fraticelli; Gabriel Castrillón; Valentin Riedl (Proceedings of the National Academy of Sciences, 2026)
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 [ 18 F]Fluorodeoxyglucose positron emission tomography acquisitions, we define metabolism-weighted centrality (MwC), a biologically grounded index of each region’s signaling dominance that integrates functional 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 broad avenues for interpreting brain vulnerability and performance.