Back

Metabolic Connectivity Gradients of the Human Brain

Deery, H. A.; Moran, C.; Liang, E.; Egan, G.; Jamadar, S. D.

2026-01-29 neuroscience
10.64898/2026.01.28.702441 bioRxiv
Show abstract

The functional architecture of the brain is organised along continuous, macro-scale gradients. However, it is unknown if the metabolic architecture of the brain displays similar gradient characteristics. Here, we use functional positron emission tomography (fPET) with 18F-fluorodeoxyglucose (FDG) to characterise the brains metabolic connectivity gradients and determine how neurobiological mechanisms shape these gradients to support cognition across the adult lifespan. We identified four principal metabolic connectivity gradients, with the primary axis recapitulating the canonical unimodal-to-transmodal hierarchy from other imaging modalities. Subsequent gradients delineated specialised dimensions of metabolic organisation, including association system differentiation, hemispheric asymmetry, and sensory system segregation. These gradients were coupled to cortical thickness, baseline rates of glucose metabolism, blood flow, and gene expression related to energy metabolism, such that transmodal, control and default mode poles were more metabolically active, more interconnected, had greater cortical thickness, and were more strongly related to the expression of genes related to cellular energy production, than unimodal and sensory poles. A reduction in gradient strength at the gradient poles was associated with older age and predicted worse cognitive performance. We conclude that the brains metabolic organisation constitutes a genetically grounded, structurally and energetically constrained gradient hierarchy that supports cognitive function and undergoes a reorganisation in ageing. TeaserThe brains metabolic architecture forms macro-scale gradients linking genes, neurobiology, and cognitive ageing.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.