Evaluating Back-to-Back and Day-to-Day Reproducibility of Cortical GABA+ Measurements Using Proton Magnetic Resonance Spectroscopy (1H MRS)
Bibliographic record
Abstract
H MRS). Considering that accurate measurements of GABA are affected by slight methodological alterations, demonstrating GABA reproducibility in healthy volunteers is essential before implementing the changes in vivo. Thus, our study aimed to evaluate the back-to-back (B2B) and day-to-day (D2D) reproducibility of GABA+ macromolecules (GABA+) using a 3 Tesla MRI scanner, the new 32-channel head coil (CHC), and Mescher-Garwood Point Resolved Spectroscopy (MEGA-PRESS) technique with the scan time (approximately 10 min), adequate for psychiatric patients. The dorsomedial pre-frontal cortex/anterior cingulate cortex (dmPFC/ACC) was scanned in 29 and the dorsolateral pre-frontal cortex (dlPFC) in 28 healthy volunteers on two separate days. Gannet 3.1 was used to quantify GABA+. The reproducibility was evaluated by Pearson's r correlation, the interclass-correlation coefficient (ICC), and the coefficient of variation (CV%) (r/ICC/CV%). For Day 1, B2B reproducibility was 0.59/0.60/5.02% in the dmPFC/ACC and 0.74/0.73/5.15% for dlPFC. For Day 2, it was 0.60/0.59/6.26% for the dmPFC/ACC and 0.54/0.54/6.89 for dlPFC. D2D reproducibility of averaged GABA+ was 0.62/0.61/4.95% for the dmPFC/ACC and 0.58/0.58/5.85% for dlPFC. Our study found excellent GABA+ repeatability and reliability in the dmPFC/ACC and dlPFC.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".