Optimized in vivo brain glutamate measurement using long‐echo‐time semi‐LASER at 7 T
Bibliographic record
Abstract
A short echo time (TE) is commonly used for brain glutamate measurement by 1H MRS to minimize drawbacks of long TE such as signal modulation due to J evolution and T2 relaxation. However, J coupling causes the spectral patterns of glutamate to change with TE, and the shortest achievable TE may not produce the optimal glutamate measurement. The purpose of this study was to determine the optimal TE for glutamate measurement at 7 T using semi‐LASER (localization by adiabatic selective refocusing). Time‐domain simulations were performed to model the TE dependence of glutamate signal energy, a measure of glutamate signal strength, and were verified against measurements made in the human sensorimotor cortex (five subjects, 2 × 2 × 2 cm3 voxel, 16 averages) on a 7 T MRI scanner. Simulations showed a local maximum of glutamate signal energy at TE = 107 ms. In vivo, TE = 105 ms produced a low Cramér‐Rao lower bound of 6.5 ± 2.0% across subjects, indicating high‐quality fits of the prior knowledge model to in vivo data. TE = 105 ms also produced the greatest glutamate signal energy with the smallest inter‐subject glutamate‐to‐creatine ratio (Glu/Cr) coefficient of variation (CV), 4.6%. Using these CVs, we performed sample size calculations to estimate the number of participants per group required to detect a 10% change in Glu/Cr between two groups with 95% confidence. 13 were required at TE = 45 ms, the shortest achievable echo time on our 7 T MRI scanner, while only 5 were required at TE = 105 ms, indicating greater statistical power. These results indicate that TE = 105 ms is optimum for in vivo glutamate measurement at 7 T with semi‐LASER. Using long TE decreases power deposition by allowing lower maximum RF pulse amplitudes in conjunction with longer RF pulses. Importantly, long TE minimizes macromolecule contributions, eliminating the requirement for acquisition of separate macromolecule spectra or macromolecule fitting techniques, which add additional scan time or bias the estimated glutamate fit.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".