Therapy-Induced Neuroplasticity in Chronic Aphasia After Phonological Component Analysis: A Matter of Intensity
Bibliographic record
Abstract
Despite the growing evidence regarding the importance of intensity and dose in aphasia therapy, few well-controlled studies contrasting the effects of intensive and non-intensive treatment have been conducted to date. Phonological components analysis (PCA) treatment for anomia has been associated with improvements in some patients with chronic aphasia; however, the effect of treatment intensity has not yet been studied with PCA. Thus, the aim of the present study was to identify the effect of intensity on neural processing associated with word retrieval abilities after PCA treatment. We used functional magnetic resonance imaging to examine therapy-induced changes in activation during an overt naming task in two patients who suffered from a stroke in the left middle cerebral artery territory. P1 received intensive PCA treatment whereas P2 received the standard, non-intensive, PCA treatment. Behavioral results indicate that both standard and intensive conditions yielded improved naming performance with treated nouns, but the changes were only significant for the patient who received the intensive treatment. The improvements were found to be long lasting as both patients maintained improved naming at 2-months follow-ups. The associated neuroimaging data indicate that the two treatment conditions were associated with different neural activation changes. The patient who received the standard PCA showed significant increase in activation with treatment in the right anterior cingulate, as well as extensive areas in bilateral posterior and lateral cortices. By contrast, the patient who received intensive PCA showed more decreases in activation following the treatment. Unexpectedly, this patient showed subcortical increase in activation, specifically in the right caudate nucleus. We speculate that the recruitment of the caudate nucleus and the anterior cingulate in these patients reflects the need to suppress errors to improve naming. Thus, both short-term intensive and standard, non-intensive, PCA treatment can improve word retrieval in chronic aphasia, but neuroimaging data suggest that improved naming is associated with different neural activation patterns in the two treatment conditions.
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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) | 0.001 | 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".