Back

Metabolic organization of macaque visual cortex reflects retinotopic eccentricity and category selectivity

Oishi, H.; Berezovskii, V. K.; Livingstone, M. S.; Weiner, K. S.; Arcaro, M. J.

2025-09-29 neuroscience
10.1101/2025.09.27.678945 bioRxiv
Show abstract

Neural activity requires energy metabolism, and the brains metabolic architecture varies across regions. Yet, it remains unclear whether these variations are meaningfully related to the functional and perceptual demands of cortical processing. In high-level visual cortex, category-selective regions, such as those preferentially responding to faces and scenes, are consistently distributed along a topographic axis that varies in sensitivity to spatial scale, a feature dimension that imposes differing metabolic demands at the level of the retina. This axis reflects a broader organizing principle of the visual system: retinotopic eccentricity, the topographic mapping of visual space relative to gaze. Here, we tested whether cortical metabolic architecture reflects this principle by aligning in vivo fMRI maps of eccentricity and visual category selectivity with ex vivo cytochrome oxidase (CO) histology, a marker of oxidative metabolism, in macaque visual cortex. We found that face-selective region ML, which is biased toward central vision, exhibits higher CO intensity than the peripherally-biased scene-selective region LPP. More broadly, CO intensity covaries with eccentricity across the entire occipitotemporal cortex, though this gradient only partially accounts for the elevated CO in ML. These findings reveal a close correspondence between cortical energy consumption and retinotopic representation, suggesting that metabolic resources are shaped by the processing demands of visual perception.

Matching journals

The top 4 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.