GABA Levels Are Decreased After Stroke and GABA Changes During Rehabilitation Correlate With Motor Improvement
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: γ-Aminobutyric acid (GABA) is the dominant inhibitory neurotransmitter in the brain and is important in motor learning. We aimed to measure GABA content in primary motor cortex poststroke (using GABA-edited magnetic resonance spectroscopy [MRS]) and in relation to motor recovery during 2 weeks of constraint-induced movement therapy (CIMT). METHODS: Twenty-one patients (3-12 months poststroke) and 20 healthy subjects were recruited. Magnetic resonance imaging structural T1 and GABA-edited MRS were performed at baseline and after CIMT, and once in healthy subjects. GABA:creatine (GABA:Cr) ratio was measured by GABA-edited MRS. Motor function was measured using Wolf Motor Function Test (WMFT). RESULTS: Baseline comparison between stroke patients (n = 19) and healthy subjects showed a significantly lower GABA:Cr ratio in stroke patients (P < .001) even after correcting for gray matter content in the voxel (P < .01) and when expressing GABA relative to N-acetylaspartic acid (NAA; P = .03). After 2 weeks of CIMT patients improved significantly on WMFT, but no consistent change across the group was observed for the GABA:Cr ratio (n = 17). However, the extent of improvement on WMFT correlated significantly with the magnitude of GABA:Cr changes (P < .01), with decreases in GABA:Cr ratio being associated with better improvements in motor function. CONCLUSIONS: In patients 3 to 12 months poststroke, GABA levels are lower in the primary motor cortex than in healthy subjects. The observed association between GABA and recovery warrants further studies on the potential use of GABA MRS as a biomarker in poststroke recovery.
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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) | 0.002 | 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".