Directed cortical connectivity inferred from neural energy metabolism

Functional connectivity (FC) from resting-state fMRI captures temporal correlations between brain regions but cannot reveal the direction of neural signalling. Determining effective connectivity, the influence of one neural system over another, is essential for understanding cortical hierarchy and its energetic constraints. We extend Metabolic Connectivity Mapping (MCM; Riedl et al., 2016), a biologically grounded framework that infers directionality by integrating FC with glucose metabolism measured via [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET). MCM builds on the principle that postsynaptic neurons consume more energy than presynaptic ones (Attwell and Laughlin, 2001; Attwell and Gibb, 2005), linking higher local metabolism to afferent input. Here, we present a new whole-cortex implementation that estimates directed connectivity directly from inter-regional energy ratios, enabling application to multimodal and fMRI-only datasets using an average cerebral metabolic rate of glucose (CMRGlc) map. The model reproduces hierarchical signalling within visual and sensorimotor systems and identifies novel directional asymmetries along sensory-cognitive gradients. MCM-derived metrics correlate with independent biological markers, including mitochondrial density (Mosharov et al., 2025) and cortical cytoarchitecture indexed by cell layer profiles (Amunts and Zilles, 2015; Wagstyl et al., 2020). By decomposing functional connectivity into metabolically constrained directed and undirected components, this framework bridges the gap between statistical connectivity and neuroenergetic mechanisms. Our results position MCM as a scalable and biologically interpretable model for inferring directed brain connectivity from human neuroimaging data.