Anomia training and brain stimulation in chronic aphasia
Bibliographic record
Abstract
Recent studies have reported enhanced performance on language tasks induced by non-invasive brain stimulation, i.e., repetitive transcranial magnetic stimulation (rTMS), or transcranial direct current stimulation (tDCS), in patients with aphasia due to stroke or Alzheimer's disease (AD). The first part of this article reviews brain stimulation studies related to language recovery in aphasic patients. The second part reports results from a pilot study with three chronic stroke patients who had non-fluent aphasia, where real or placebo rTMS was immediately followed by 25 minutes of individualised speech therapy. Real rTMS consisted of high-frequency rTMS over the left dorsolateral prefrontal cortex (BA 8/9) for 25 minutes. Each patient underwent a total of four weeks of intervention. P1 underwent four weeks of real rTMS (5 days/week) where individualised speech therapy was provided for 25 minutes immediately following each rTMS session. P2 and P3 each underwent two weeks of placebo rTMS, followed immediately by individualised speech therapy; then two weeks of real rTMS, followed immediately by individualised speech therapy. Assessments took place at 2, 4, 12, 24 and 48 weeks post-entry/baseline testing. Relative to entry/baseline testing, a significant improvement in object naming was observed at all testing times, from two weeks post-intervention in real rTMS plus speech therapy, or placebo rTMS plus speech therapy. Our findings suggest beneficial effects of targeted behavioural training in combination with brain stimulation in chronic aphasic patients. However, further work is required in order to verify whether optimal combination parameters (rTMS alone or speech therapy alone) and length of rTMS treatment may be found.
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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".