In Vivo Quantification of Creatine Kinase Kinetics in Mouse Brain Using <sup>31</sup>P‐MRS at 7 T
Bibliographic record
Abstract
ABSTRACT 31P‐MRS is a method of choice for studying neuroenergetics in vivo, but its application in the mouse brain has been limited, often restricted to ultrahigh field (> 7 T) MRI scanners. Establishing its feasibility on more readily available preclinical 7‐T scanners would create new opportunities to study metabolism and physiology in murine models of brain disorders. Here, we demonstrate that the apparent forward rate constant (kf) of creatine kinase (CK) can be accurately quantified using a progressive saturation‐transfer approach in the mouse brain at 7 T. We also find that a 20% reduction in respiration of anesthetized mice can lead to 36% increase in kf attributable to a drop in cellular pH and mitochondrial ATP production. To achieve this, we used a test–retest analysis to assess the reliability and repeatability of 31P‐MRS acquisition, analysis, and experimental design protocols. We report that many 31P‐containing metabolites can be reliably measured using a localized 3D‐ISIS sequence, which showed highest SNR amplitude, SNR consistency, and minimal T2 relaxation signal loss. Our study identifies key physiological factors influencing mouse brain energy homeostasis in vivo and provides a methodological basis to guide future studies interested in implementing 31P‐MRS on preclinical 7‐T scanners.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".