Functional Changes in GABA and Glutamate during Motor Learning
Bibliographic record
Abstract
Functional magnetic resonance spectroscopy (fMRS) of GABA at 3 T poses additional challenges compared with fMRS of other metabolites because of the difficulties of measuring GABA levels; GABA is present in the brain at relatively low concentrations, and its signal is overlapped by higher concentration metabolites. Using 7 T fMRS, GABA levels have been shown to decrease specifically during motor learning (and not during a control task). Though the use of 7 T is appealing, access is limited. For GABA fMRS to be widely accessible, it is essential to develop this method at 3 T. Nine healthy right-handed participants completed a motor learning and a control button-pressing task. fMRS data were acquired from the left sensorimotor cortex during the task using a continuous GABA-edited MEGA-PRESS acquisition at 3 T. We found no significant changes in GABA+/tCr, Glx/tCr, or Glu/tCr levels in either task; however, we show a positive relationship between motor learning and glutamate levels both at rest and at the start of the task. Though further refinement and validation of this method is needed, this study represents a further step in using fMRS at 3 T to probe GABA levels in both healthy cognition and clinical disorders.
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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) | 0.001 | 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".