Examination of methods to separate overlapping metabolites at 7T
Bibliographic record
Abstract
PURPOSE: Neurochemicals of interest quantified by MRS are often composites of overlapping signals. At higher field strengths (i.e., 7T), there is better separation of these signals. As the availability of higher field strengths is increasing, it is important to re-evaluate the separability of overlapping metabolite signals. METHODS: This study compares the ability of stimulated echo acquisition mode (STEAM-8; TE = 8 ms), short-TE semi-LASER (sLASER-34; TE = 34 ms), and long-TE semi-LASER (sLASER-105; TE = 105 ms) acquisitions to separate the commonly acquired neurochemicals at 7T (Glx, consisting of glutamate and glutamine; total N-acetyl aspartate, consisting of N-acetyl aspartate and N-acetylaspartylglutamate; total creatine, consisting of creatine and phosphocreatine; and total choline, consisting of choline, phosphocholine, and glycerophosphocholine). RESULTS: sLASER-34 produced the lowest fit errors for most neurochemicals; however, STEAM-8 had better within-subject reproducibility and required fewer subjects to detect a change between groups. However, this is dependent on the neurochemical of interest. CONCLUSION: We recommend short-TE STEAM for separation of most standard neurochemicals at 7T over short-TE or long-TE sLASER.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".