Improved resolution of glutamate, glutamine and γ‐aminobutyric acid with optimized point‐resolved spectroscopy sequence timings for their simultaneous quantification at 9.4 T
Bibliographic record
Abstract
Glutamine (Gln), glutamate (Glu) and γ‐aminobutyric acid (GABA) are relevant brain metabolites that can be measured with magnetic resonance spectroscopy (MRS). This work optimizes the point‐resolved spectroscopy (PRESS) sequence echo times, TE 1 and TE 2 , for improved simultaneous quantification of the three metabolites at 9.4 T. Quantification was based on the proton resonances of Gln, Glu and GABA at ≈2.45, ≈2.35 and ≈2.28 ppm, respectively. Glu exhibits overlap with both Gln and GABA; in addition, the Gln peak is contaminated by signal from the strongly coupled protons of N ‐acetylaspartate (NAA), which resonate at about 2.49 ppm. J ‐coupling evolution of the protons was characterized numerically and verified experimentally. A {TE 1 , TE 2 } combination of {106 ms, 16 ms} minimized the NAA signal in the Gln spectral region, whilst retaining Gln, Glu and GABA peaks. The efficacy of the technique was verified on phantom solutions and on rat brain in vivo . LCModel was employed to analyze the in vivo spectra. The average T 2 ‐corrected Gln, Glu and GABA concentrations were found to be 3.39, 11.43 and 2.20 mM, respectively, assuming a total creatine concentration of 8.5 mM. LCModel Cramér–Rao lower bounds (CRLBs) for Gln, Glu and GABA were in the ranges 14–17%, 4–6% and 16–19%, respectively. The optimal TE resulted in concentrations for Gln and GABA that agreed more closely with literature concentrations compared with concentrations obtained from short‐TE spectra acquired with a {TE 1 , TE 2 } combination of {12 ms, 9 ms}. LCModel estimations were also evaluated with short‐TE PRESS and with the optimized long TE of {106 ms, 16 ms}, using phantom solutions of known metabolite concentrations. It was shown that concentrations estimated with LCModel can be inaccurate when combined with short‐TE PRESS, where there is peak overlap, even when low (<20%) CRLBs are reported.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".