Cardiopulmonary Fitness Correlates with Regional Cerebral Grey Matter Perfusion and Density in Men with Coronary Artery Disease
Bibliographic record
Abstract
PURPOSE: Physical activity is associated with positive effects on the brain but there is a paucity of clinical neuroimaging data in patients with coronary artery disease (CAD), a cardiovascular condition associated with grey matter loss. The purpose of this study was to determine which brain regions are impacted by cardiopulmonary fitness and with the change in fitness after 6 months of exercise-based cardiac rehabilitation. METHODS: CAD patients underwent magnetic resonance imaging at baseline, and peak volume of oxygen uptake during exercise testing (VO2Peak) was measured at baseline and after 6 months of training. T1-weighted structural images were used to perform grey matter (GM) voxel-based morphometry (VBM). Pseudo-continuous arterial spin labeling (pcASL) was used to produce cerebral blood flow (CBF) images. VBM and CBF data were tested voxel-wise using VO2Peak and age as explanatory variables. RESULTS: In 30 men with CAD (mean age 65±7 years), VBM and CBF identified 7 and 5 respective regions positively associated with baseline VO2Peak. These included the pre- and post-central, paracingulate, caudate, hippocampal regions and converging findings in the putamen. VO2Peak increased by 20% at follow-up in 29 patients (t = 9.6, df = 28, p<0.0001). Baseline CBF in the left post-central gyrus and baseline GM density in the right putamen predicted greater change in VO2Peak. CONCLUSION: Perfusion and GM density were associated with fitness at baseline and with greater fitness gains with exercise. This study identifies new neurobiological correlates of fitness and demonstrates the utility of multi-modal MRI to evaluate the effects of exercise in CAD patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".