Quantifying Language Tract Damage in Stroke Patients: Utilizing Diffusion Tractography and Streamline Counts to Predict Aphasia Scores
Bibliographic record
Abstract
Aphasia is a neurological condition that challenges language production and comprehension. It often follows a stroke that damages critical language regions in the brain. While existing research has highlighted the roles of grey matter lesions in aphasia, the potential of white matter damage as a predictive marker remains underexplored. This study investigates the feasibility of using diffusion tractography to assess white matter integrity via interhemispheric streamline count difference and subsequently investigates whether this white matter damage measure can be used to predict aphasia severity over and above existing predictors like grey matter damage. Our findings reveal significant negative correlations between white matter damage, assessed through interhemispheric streamline count differences and mean fractional anisotropy discrepancies, and aphasia severity. Multiple regression analyses subsequently underscored the importance of incorporating streamline count as an additional predictor of Western Aphasia Battery (WAB) scores alongside grey matter lesion volume, highlighting their collective contribution to aphasia severity prediction. By elucidating the relationship between white matter integrity and language deficits, our study offers insights into the development of automated diagnostic tools aiming to improve outcomes for aphasia patients.
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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 | 0.003 |
| 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) | 0.001 | 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".