In vivo Glx and Glu measurements from GABA‐edited MRS at 3 T
Bibliographic record
Abstract
In vivo quantification of glutamate (Glu) and γ‐aminobutyric acid (GABA) using MRS is often achieved using two separate sequences: a short‐echo point resolved spectroscopy (PRESS) acquisition for Glu and a Mescher‐Garwood PRESS (MEGA‐PRESS) acquisition for GABA. The purpose of this study was to examine the agreement of Glu and Glx (the combined signal of glutamate + glutamine) quantified from two different GABA‐edited MEGA‐PRESS acquisitions (GABA plus macromolecules, GABA+, T E = 68 ms, and macromolecule suppressed, MMSup, T E = 80 ms) with Glu and Glx quantified from a short‐echo PRESS (PRESS‐35, T E = 35 ms) acquisition. Fifteen healthy male volunteers underwent a single scan session, in which data were acquired using the three acquisitions (GABA+, MMSup and PRESS‐35) in both the sensorimotor and anterior cingulate cortices using a voxel size of 3 × 3 × 3 cm 3 . Glx and Glu were quantified from the MEGA‐PRESS data using both the OFF sub‐spectra and the difference (DIFF) spectra. Agreement was assessed using correlation analyses, Bland–Altman plots and intraclass correlation coefficients. Glx quantified from the OFF sub‐spectra from both the GABA+ and MMSup acquisitions showed poor agreement with PRESS‐35 in both brain regions. In the sensorimotor cortex, Glu quantified from the OFF sub‐spectra of GABA+ showed moderate agreement with PRESS‐35 data, but this finding was not replicated in the anterior cingulate cortex. Glx and Glu quantified using the DIFF spectra of either MEGA‐PRESS sequence were in poor agreement with the PRESS‐35 data in both brain regions. In conclusion, Glx and Glu measured from MEGA‐PRESS data generally showed poor agreement with Glx and Glu measured using PRESS‐35.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".