Abstract TP171: Eloquence of White Matter Tracts in Acute Ischemic Stroke Patients
Bibliographic record
Abstract
Background: Clinical assessment scores after acute ischemic stroke are only moderately correlated with structural brain damage since lesion location is also an important confounding factor. Many studies have investigated gray matter eloquence but, general understanding about the importance of specific white matter (WM) tract involvement is limited. The aim of this study was to investigate the eloquence of major WM tracts with respect to 24 hours post-stroke NIHSS. Methods: Lesions in follow-up FLAIR MRI datasets acquired 1-7 days after acute stroke onset due to proximal MCA or ICA occlusion were manually segmented and non-linearly registered to a common atlas. Voxel-based lesion-symptom mapping (VLSM) using sparse canonical correlation analysis was used to generate a statistical eloquence map with normalized t-scores ranging from 0 to 1 with the 24h NIHSS as outcome score. The JHU probabilistic WM atlas was used to quantify the individual tract eloquence. Results: 96 patients were included in this study (50 females, mean age 66.4±14.0 years, median NIHSS 5, IQR 2-9.5). Multivariate VLSM resulted in a major left-hemispheric and smaller right-hemispheric cluster of significant voxels overlapping with four WM tracts. The left corticospinal tract (motor function) showed an overlap of 3.52% (maximum t-score: 0.31; mean: 0.17±0.05), the right corticospinal tract an overlap of 7.33% (maximum t-score: 0.54; mean: 0.26±0.01), the left anterior thalamic radiation (sensory and motor relay) an overlap of 5.89% (maximum t-score: 0.44, mean: 0.18±0.07), and the inferior fronto-occipital fasciculus (auditory and visual association) an overlap of 21.31% (maximum t-score: 0.34, mean: 0.21±0.06). Conclusions: The significantly affected WM tracts identified are related to motor and cognition function, predominantly assessed by NIHSS, highlighting the importance of white matter tract involvement for recovery and rehabilitation.
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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 | 0.001 | 0.001 |
| 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) | 0.003 | 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".