Effects of tissue and gender on macromolecule suppressed gamma‐aminobutyric acid
Bibliographic record
Abstract
Abstract The aims of this study are to determine the ratio of concentration of GABA in grey matter to concentration of GABA in white matter (GABAGM/GABAWM) and compare with literature values, and to investigate gender‐related differences in GABA in healthy subjects. Twenty healthy subjects were scanned using a motion and shim navigated MEGA‐SPECIAL MRS sequence. For every subject, two acquisitions were performed for each of two regions, the anterior cingulate cortex (ACC) and medial‐parietal cortex (PAR), with the order interleaved. Absolute GABA (GABAH2O) and GABA/Cr concentrations were measured using LCModel. LCModel fitting revealed an overall average Cramer–Rao Lower Bounds ≈10%. The GABAGM/GABAWM for both GABAH2O and GABA/Cr are 3 and 1.5, respectively, and in agreement with the literature. The ACC revealed no gender‐related differences in GABA. The PAR revealed time‐related changes in concentrations of GABA in male participants and thus gender‐related differences. The higher concentration of GABA in GM than in WM found in this study and literature might be reflective of heterogeneous distribution of GABA around the brain. The gender‐ and time‐related differences in the PAR emphasize that gender‐ and time‐matching are critical for GABA scans.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".