Dynamic <sup>1</sup>H‐MRS for detection of <sup>13</sup>C‐labeled glucose metabolism in the human brain at 3T
Bibliographic record
Abstract
Purpose In 2004, Boumezbeur et al proposed a simple yet powerful approach to detect the metabolism of 13C‐enriched substrates in the brain. Their approach consisted of dynamic 1H‐MRS, without a 13C radiofrequency (RF) channel, and its successful application was demonstrated in monkeys. Since then, this promising method has yet to be applied rigorously in humans. In this study, we revisit the use of dynamic 1H‐MRS to measure the metabolism of 13C‐enriched substrates and demonstrate its application in the human brain. Methods In healthy participants, 1H‐MRS data were acquired dynamically before and following a bolus infusion of [1‐13C] glucose. Data were acquired on a 3T clinical MRI scanner using a short‐TE SPECIAL sequence, with regions of interest in both anterior and posterior cingulate cortex. Using simulated basis spectra to model signal changes in both 12C‐bonded and 13C‐coupled resonances, the acquired spectra were fit in LCModel to obtain labeling time courses for glutmate and glutamine at both C4 and C3 positions. Results Presence of the 13C label was clearly detectable, owing to the pronounced effect of heteronuclear (13C‐1H) scalar coupling on the observed 1H spectra. A decrease in signal from 12C‐bonded protons and an increase in signal from 13C‐coupled protons were observed. The fractional enrichment of Glu‐C4, (Glu+Gln)‐C4, and (Glu+Gln)‐C3 at 30 minutes following infusion of [1‐13C] glucose was similar in both regions: 11% to 13%, 9% to 12% and 3% to 5%, respectively. Conclusion These preliminary results confirm the feasibility of the use of dynamic 1H‐MRS to monitor 13C labeling in the human brain, without a 13C RF channel.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".