A right convergence area of the prefrontal lobe is involved in the improvement of semantic fluency in patients with post-stroke aphasia
Bibliographic record
Abstract
Objectives This study aimed to longitudinally observe the improvement mechanism of semantic fluency in subacute post-stroke aphasia (PSA) patients using resting-state functional magnetic resonance imaging (rs-fMRI).Methods Twelve PSA patients, about one month after onset, were enrolled in this study and received speech-language therapy (SLT) for one month. Auditory comprehension and semantic fluency were evaluated using the Western Aphasia Battery (WAB) and the Animal Fluency Test. Before and after treatment, rs-fMRI data were collected, and the dice similarity coefficient was used to measure the spatial similarity between each patient’s lesion and a reference lesion. The left posterior inferior temporal gyrus (pITG) was used as a seed to calculate the normalized functional connectivity in whole-brain voxel analysis using DPABI software for statistical analysis.Results The dice similarity coefficient between each patient’s lesion and the reference lesion showed moderate to high intensity (0.57 ± 0.14) in the Montreal Neurological Institute space. After treatment, we found a significant increase in functional connectivity between the left pITG and the right prefrontal lobe convergence area (peak t = 8.219, Gaussian random field multiple comparison correction, voxel p < 0.001, cluster p < 0.05). The increase in functional connectivity was negatively correlated with the improvement in auditory comprehension (r =–0.707, p = 0.033) and positively correlated with the improvement in semantic fluency (r = 0.79, p = 0.02).Conclusion The improvement of semantic fluency in subacute PSA patients may require the participation of the right convergence area of the prefrontal lobe.
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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".