Spatial distribution of hyperpolarized [1-13C]pyruvate MRI and metabolic PET in the human brain
Bibliographic record
Abstract
Abstract Magnetic resonance imaging (MRI) of hyperpolarized (HP) [1-13C]pyruvate is a promising method for measuring cerebral energy metabolism in vivo. The substantial increase in signal provided by HP makes it possible to dynamically monitor the conversion of [1-13C]pyruvate to [1-13C]lactate and [13C]bicarbonate. The HP [1-13C]lactate signal is commonly associated with glycolic activity, whereas [13C]bicarbonate, a by-product of the reaction that forms acetyl-CoA, is linked to oxidative metabolism. However, there is compelling evidence that other factors, such as the concentration of monocarboxylate transporters, influence the production of HP [1-13C]lactate. To clarify the processes responsible for producing the topography of HP [1-13C]pyruvate and its metabolites, we spatially correlated group-average HP 13C MRI images with [18F]FDG, [15O]H2O, [15O]O2, and [15O]CO positron emission topography (PET) images from a separate group of 35 age- and sex-matched adults. We found that [1-13C]pyruvate correlated best with cerebral blood volume (CBV), whereas [1-13C]lactate and [13C]bicarbonate were most strongly associated with cerebral blood flow (CBF), glucose consumption (CMRglc), and oxygen metabolism (CMRO2). Neither [1-13C]lactate nor [13C]bicarbonate was correlated with non-oxidative glucose consumption, also known as aerobic glycolysis. These results are consistent with the view that in the healthy brain, the production of [1-13C]lactate reflects overall energy metabolism rather than being specific to glycolysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".