Measurement of GABA and contaminants in gray and white matter in human brain in vivo
Bibliographic record
Abstract
A preliminary study of discrimination between GABA and macromolecules (MMs) in human brain by proton double quantum filtering (DQF) at 3.0 T in vivo is presented. GABA-tuned and MM-tuned DQ filters were designed with dual-band 180 degrees radiofrequency (RF) pulses that were tuned for selective refocusing of GABA (3.0 and 1.9 ppm) and putative MM resonances (3.0 and 1.7 ppm), respectively. GABA and putative MM signals were extracted from a combined analysis of the filtered mixture signals and the calculated editing yields. Unexpectedly, the GABA and putative MM signals exhibited a similar doublet linewidth at the optimized TE = 82 ms. Furthermore, substantial MM-tuned DQF signal remained at TE = 148 ms, indicating the presence of a component other than MM. With water segmentation data, the GABA-tuned and MM-tuned DQF measures from the medial prefrontal and left frontal lobes were combined to give the concentrations of GABA and the additional component as 1.1 +/- 0.1 and 0.8 +/- 0.1 mM (mean +/- SD, N=3) for gray matter (GM) and 0.4 +/- 0.1 and 0.7+/-0.1 mM (N=3) for white matter (WM), respectively.
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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 | 0.000 |
| 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) | 0.001 | 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".