White Matter Microstructural Integrity Is Associated with Executive Function and Processing Speed in Older Adults with Coronary Artery Disease
Bibliographic record
Abstract
OBJECTIVE: Coronary artery disease (CAD) is associated with an increased risk of cognitive decline. Although cerebral white matter (WM) damage predicts cognitive function in CAD, conventional neuroimaging measures only partially explain the effect of CAD on cognition. The purpose of this study was to determine if WM microstructural integrity and CAD using diffusion tensor imaging (DTI) correlates with cognitive function in older adults with CAD. METHODS: Forty-nine CAD patients (66 ± 7 years old, 86% male) underwent neurocognitive assessments using the cognitive battery recommended by the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network for the study of vascular cognitive impairment. Composite scores for each cognitive domain were calculated. Microstructural integrity in normal-appearing WM was quantified as fractional anisotropy (FA) using DTI in nine bilateral and two interhemispheric WM tracts from the Johns Hopkins University WM Tractography Atlas. Linear regression models examined associations between FA and cognitive performance, controlling for age, sex, and education, with correction for multiple comparisons using a false discovery rate of 5%. RESULTS: Executive function was most significantly associated with FA in the left parahippocampal cingulum (β = 0.471, t = 3.381, df = 44, p = 0.002) and left inferior fronto-occipital fasciculus (β = 0.430, t = 2.984, df = 44, p = 0.005). FA was not associated with memory in any of the WM tracts examined. CONCLUSION: These results suggest that WM microstructural integrity may be an important neural correlate of executive function even in cognitively intact CAD patients. This study suggests WM damage may be relevant to subtle cognitive decline in a population that may have early neural risk for dementia.
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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 | 0.002 |
| 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) | 0.001 | 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".