Effects of Noninvasive Brain Stimulation on Language Networks and Recovery in Early Poststroke Aphasia
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Modulation of activity in language networks using repetitive transcranial magnetic stimulation (rTMS) may possibly support recovery from poststroke aphasia. Case series and feasibility studies seem to indicate a therapeutic effect; however, randomized sham-controlled, proof-of-principle studies relating clinical effects to activation patterns are missing. METHODS: Twenty-four patients with subacute poststroke aphasia were randomized to a 10-day protocol of 20-minute inhibitory 1 Hz rTMS over the right triangular part of the posterior inferior frontal gyrus or sham stimulation, followed by 45 minutes of speech and language therapy. Activity in language networks was measured with O-15-water positron emission tomography during verb generation before and after treatment. Language performance was assessed using the Aachen Aphasia Test battery. RESULTS: The primary outcome measure, global Aachen Aphasia Test score change, was significantly higher in the rTMS group (t test, P=0.003). Increases were largest for subtest naming (P=0.002) and tended to be higher for comprehension, token test, and writing (P<0.1). Patients in the rTMS group activated proportionally more voxels in the left hemisphere after treatment than before (difference in activation volume index) compared with sham-treated patients (t test, P=0.002).There was a moderate but significant linear relationship between activation volume index change and global Aachen Aphasia Test score change (r2=0.25; P=0.015). CONCLUSIONS: Ten sessions of inhibitory rTMS over the right posterior inferior frontal gyrus, in combination with speech and language therapy, significantly improve language recovery in subacute ischemic stroke and favor recruitment of left-hemispheric language networks.
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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) | 0.002 | 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".