Ashrafi, M. | bioRxiv Science

bioRxiv2026

Directed cortical connectivity inferred from neural energy metabolism

Complex cognition depends on directed communication between brain regions. Yet, resting functional MRI resolves only undirected associations, and inferring the direction of signalling has proven difficult across the whole brain. Because synaptic transmission is metabolically asymmetric, with the postsynaptic neuron bearing most of the energetic cost, regional energy demand carries a physical signature of the direction of communication. Here, we show that this signature organizes the human cortex into a reproducible directed hierarchy, in which sensory and attentional systems preferentially drive higher-order networks, while the default mode and control networks act as principal receivers. Critically, the inferred directionality is independently predicted by two cellular markers of neuronal energy demand, regional mitochondrial density and laminar cytoarchitecture, linking macroscale information flow to its cellular substrate. The directed architecture recapitulates the known feedforward and feedback organization of visual pathways and, using an average CMRGlc template, extends to functional MRI acquired without PET. By grounding communication direction in energy metabolism, Metabolic Connectivity Mapping bridges correlative and effective connectivity and offers a scalable, biologically interpretable framework for mapping directed communication across the human brain.

neuroscience↗

bioRxiv2025

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 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 StatementThe 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 regions 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 Alzheimers 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.

neuroscience↗