Training-Induced Brain Remapping in Chronic Aphasia: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: The neural correlates of training-induced improvements of cognitive functions after brain damage remain still scarcely understood. In the specific case of aphasia, although several investigations have addressed the issue of the neural substrates of functional recovery, only a few studies have attempted to assess the impact of language training on the damaged brain. AIMS: The main goal of this study was to examine the neurobiological correlates of improved picture-naming performance in 2 aphasic patients who received intensive and specific training for a chronic and severe phonological anomia. METHODS: In both participants, picture-naming performance was assessed before and after phonological cueing training. Training-induced changes in patients' performance were correlated to brain activity patterns as revealed by pre- and post-training event-related functional magnetic resonance imaging scanning. RESULTS: Training-induced improvement was observed concurrently with changes in the brain activation patterns. Better performance was observed in the patient with the smaller lesion, partially sparing Broca's area, who showed a left perilesional reactivation. Conversely, the patient with complete destruction of Broca's area showed a posttraining activation in the right mirror frontal region. CONCLUSIONS: The results show that, even in the chronic stage, phonological strategies may improve impaired naming and induce cerebral reorganization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".