The brain's "dark energy" puzzle upgraded: [¹⁸F]FDG uptake, delivery and phosphorylation, and their coupling with...

The brain's resting-state energy consumption is expected to be mainly driven by spontaneous activity. In our previous work, we extracted a wide range of features from resting-state fMRI (rs-fMRI), and used them to predict [¹⁸F]FDG PET SUVR as a proxy of glucose metabolism. Here, we expanded upon our previous effort by estimating [¹⁸F]FDG kinetic parameters according to Sokoloff's model, i.e., $K_i$ (irreversible uptake rate), $K_1$ (delivery), $k_3$ (phosphorylation), in a large healthy control group. The parameters' spatial distribution was described at a high spatial resolution. We showed that while $K_1$ is the least redundant, there are relevant differences between $K_i$ and $k_3$ (occipital cortices, cerebellum and thalamus). Using multilevel modeling, we investigated how much of the regional variability of [¹⁸F]FDG parameters could be explained by a combination of rs-fMRI variables only, or with the addition of cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO₂), estimated from ¹⁵O PET data. We found that combining rs-fMRI and CMRO₂ led to satisfactory prediction of individual $K_i$ variance (45%). Although more difficult to describe, $K_i$ and $k_3$ were both most sensitive to local rs-fMRI variables, while $K_1$ was sensitive to CMRO₂. This work represents the most comprehensive assessment to date of the complex functional and metabolic underpinnings of brain glucose consumption.