Trail Making Test Elucidates Neural Substrates of Specific Poststroke Executive Dysfunctions
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Poststroke cognitive impairment is typified by prominent deficits in processing speed and executive function. However, the underlying neuroanatomical substrates of executive deficits are not well understood, and further elucidation is needed. There may be utility in fractionating executive functions to delineate neural substrates. METHODS: One test amenable to fine delineation is the Trail Making Test (TMT), which emphasizes processing speed (TMT-A) and set shifting (TMT-B-A difference, proportion, quotient scores, and TMT-B set-shifting errors). The TMT was administered to 2 overt ischemic stroke cohorts from a multinational study: (1) a chronic stroke cohort (N=61) and (2) an acute-subacute stroke cohort (N=45). Volumetric quantification of ischemic stroke and white matter hyperintensities was done on magnetic resonance imaging, along with ratings of involvement of cholinergic projections, using the previously published cholinergic hyperintensities projections scale. Damage to the superior longitudinal fasciculus, which colocalizes with some cholinergic projections, was also documented. RESULTS: Multiple linear regression analyses were completed. Although larger infarcts (β=0.37, P<0.0001) were associated with slower processing speed, cholinergic hyperintensities projections scale severity (β=0.39, P<0.0001) was associated with all metrics of set shifting. Left superior longitudinal fasciculus damage, however, was only associated with the difference score (β=0.17, P=0.03). These findings were replicated in both cohorts. Patients with ≥2 TMT-B set-shifting errors also had greater cholinergic hyperintensities projections scale severity. CONCLUSIONS: In this multinational stroke cohort study, damage to lateral cholinergic pathways and the superior longitudinal fasciculus emerged as significant neuroanatomical correlates for executive deficits in set shifting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".