Sources of Variability and Improvements in the Measure of GABA using MEGA-PRESS at 3T
Bibliographic record
Abstract
With the emergence of research investigating the role of gamma-aminobutyric acid (GABA) in neurological and neuropsychiatric diseases, it is important to understand the variability in GABA as measured through magnetic resonance spectroscopy (MRS) and how this variability may affect the interpretation of results. This review addresses methodological sources of variation documented in the current literature on the measurement of GABA. GABA differences related to hardware, acquisition, post-processing and quantification are discussed, and methods to account for or remove this variability are highlighted. Additionally, factors such as time, age, biological sex, hormones, and brain region, which may affect GABA MRS, are reviewed to aid in study design. This review is meant to assist in measuring GABA comparably between studies and improve GABA methodology to allow the use of GABA MRS in a clinical setting in the future.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".