Unedited <i>in vivo</i> detection and quantification of γ‐aminobutyric acid in the occipital cortex using short‐TE MRS at 3 T
Bibliographic record
Abstract
Short-TE MRS has been proposed recently as a method for the in vivo detection and quantification of γ-aminobutyric acid (GABA) in the human brain at 3 T. In this study, we investigated the accuracy and reproducibility of short-TE MRS measurements of GABA at 3 T using both simulations and experiments. LCModel analysis was performed on a large number of simulated spectra with known metabolite input concentrations. Simulated spectra were generated using a range of spectral linewidths and signal-to-noise ratios to investigate the effect of varying experimental conditions, and analyses were performed using two different baseline models to investigate the effect of an inaccurate baseline model on GABA quantification. The results of these analyses indicated that, under experimental conditions corresponding to those typically observed in the occipital cortex, GABA concentration estimates are reproducible (mean reproducibility error, <20%), even when an incorrect baseline model is used. However, simulations indicate that the accuracy of GABA concentration estimates depends strongly on the experimental conditions (linewidth and signal-to-noise ratio). In addition to simulations, in vivo GABA measurements were performed using both spectral editing and short-TE MRS in the occipital cortex of 14 healthy volunteers. Short-TE MRS measurements of GABA exhibited a significant positive correlation with edited GABA measurements (R = 0.58, p < 0.05), suggesting that short-TE measurements of GABA correspond well with measurements made using spectral editing techniques. Finally, within-session reproducibility was assessed in the same 14 subjects using four consecutive short-TE GABA measurements in the occipital cortex. Across all subjects, the average coefficient of variation of these four GABA measurements was 8.7 ± 4.9%. This study demonstrates that, under some experimental conditions, short-TE MRS can be employed for the reproducible detection of GABA at 3 T, but that the technique should be used with caution, as the results are dependent on the experimental conditions.
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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 | 0.002 |
| 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".