Subcortical Brain Involvement Is Associated With Impaired Performance on the Psychomotor Vigilance Task After Minor Stroke
Bibliographic record
Abstract
OBJECTIVE: Impaired attentional processes have been linked with poor outcomes after stroke, but their radiographical correlates have been infrequently studied. Our objective was to assess the relationship between stroke location and vigilant attention. METHODS: A total of 39 patients presenting within 2 weeks of a minor stroke were prospectively recruited. Vigilant attention was assessed using the psychomotor vigilance task (PVT), and neuroimaging was used to assess stroke location, white matter hyperintensity (WMH) burden, and ischemic stroke involvement within lateral cholinergic projections. Correlational analyses and linear regression models tested the association between PVT performance and our neuroimaging parameters of interest. Subtractions of lesion overlays were used to identify brain regions of acute stroke patients who performed most poorly on the PVT. RESULTS: Subcortical stroke location was a predictor of PVT performance in this cohort of acute stroke patients. Patients who performed most poorly on the PVT had lesions in the corona radiata, internal capsule, globus pallidus, and thalamus. Global WMH burden and cerebrovascular disease in lateral cholinergic pathways were not significant predictors of PVT performance. INTERPRETATION: Subcortical stroke location was associated with impaired vigilant attention. The poorest PVT performers had stroke lesions involving the corona radiata, internal capsule, globus pallidus, and thalamus, suggesting that vigilance depends on the integrity of subcortical structures and their connections with cortical brain regions.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".