Dissociation of white matter bundles in different recovery measures in post-stroke aphasia
Bibliographic record
Abstract
ABSTRACT Background Post-stroke aphasia (PSA) recovery shows high variability across individuals and at different moments during recovery. Although diffusion biomarkers from the ventral and dorsal streams have demonstrated strong predictive power for language outcomes, it is still unclear how these biomarkers relate to the various stages of PSA recovery. In this study, we aim to compare diffusion metrics and language measures as predictors of language recovery in a longitudinal cohort of participants with PSA. Methods Twenty-four participants (mean age = 73 years, 8 women) presenting PSA were recruited in an acute stroke unit. Participants underwent diffusion MRI scanning and language assessment within 3 days (acute phase) after stroke, with a behavioral follow-up at subacute (10±3 days) and chronic phases (> 6 months). We used regression analyses on language performance (cross-sectional) and Δscores at subacute and chronic timepoints (difference between acute and subacute, and subacute and chronic respectively), with language baseline scores, diffusion metrics from language-related white matter tracts, lesion size and demographic predictors. Results Best prediction model of performance scores used axial diffusivity (AD) from the left arcuate fasciculus (AF) in both subacute (R 2 = 0.785) and chronic timepoints (R 2 = 0.626). Moreover, prediction of change scores depended on AD from left inferior frontal-occipital fasciculus (IFOF), in subacute stage (R 2 = 0.5), and depended additionally on AD from right IFOF in the chronic stages (R 2 = 0.68). Mediation analyses showed that lesion load of left AF mediated the relationship between AD from left AF and chronic language performance. Conclusion Language performance in subacute and chronic timepoints depends on the integrity of left AF, whereas Δscores of subacute and chronic phases depends on left IFOF, showing a dissociation of the white matter pathways regarding language outcomes. These results support the hypothesis of a functional differentiation of the dual-stream components in PSA recovery.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".