Reproducibility of HERMES-measured GABA+ and glutathione in the mesial temporal lobe
Bibliographic record
Abstract
BACKGROUND: There is growing interest in using Hadamard Encoding and Reconstruction for MEGA-Edited Spectroscopy (HERMES) within the mesial temporal lobe (MTL). For cross-sectional group comparisons and longitudinal repeated measures designs, an understanding of the internal and test-retest validity of γ-aminobutyric acid (GABA+) and glutathione (GSH) is critical. We therefore evaluated the reproducibility of the consensus recommended semi-localization by adiabatic selective refocusing (sLASER) localization for edited-MRS acquisitions in a challenging region, the MTL. NEW METHOD: Data were acquired in 15 participants. Single voxel HERMES was collected in the left MTL (two acquisitions) and the right MTL (one acquisition). Participants were repositioned between the two left HERMES acquisitions. An ANOVA was used to assess differences between acquisitions. To assess measurement variation in the repeated left of GABA+ and GSH measures within the left MTL difference values and coefficients of variation (CVs) were calculated. RESULTS: There were no significant differences in metabolite values between any of the acquisitions. The mean difference between the metabolite measures from the repeated left acquisitions centred close to zero, and the average CVs were 14.09 % for GABA+ and 18.94 % for GSH. COMPARISON WITH EXISTING METHODS: The CVs of GABA+ and GSH in the MTL obtained from a HERMES acquisition were comparable to GABA+ or GSH-edited acquisitions in this region, and to data from cortical voxels using HERMES acquisitions. CONCLUSIONS: This supports the use of HERMES in the MTL, a challenging region for MRS. However, larger samples and caution in interpretation may be required in repeated-measures designs.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".