The Neural Correlates of Semantic Feature Analysis in Chronic Aphasia: Discordant Patterns According to the Etiology
Bibliographic record
Abstract
This event-related functional magnetic resonance imaging (fMRI) study reports on the impact of semantic feature analysis (SFA) therapy on the neural substrate sustaining the recovery from severe anomia in two patients: one participant was diagnosed with primary progressive aphasia (PPA) 2 years before this study; the other participant acquired aphasia 8 years before this study. The participant with PPA showed severe progressive nonfluent aphasia (PNFA), the language profile being similar to a Broca's aphasia; the stroke patient presented with Broca's aphasia and a severe apraxia of speech (AOS). To examine the neural substrate allowing for recovery, both patients received brief and intensive therapy with SFA; behavioral and event-related (ER)-fMRI measures during oral picture naming were obtained pre- and post-therapy. Both patients benefitted from SFA to improve their naming performance. Functional MRI performances on trained and correct pretraining items were contrasted. Adaptive brain plasticity appeared to operate differently in each patient, despite the similarity of naming recovery profiles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".