Coronary artery disease affects cortical circuitry associated with brain-heart integration during volitional exercise
Bibliographic record
Abstract
This study tested the hypothesis that coronary artery disease (CAD) alters the cortical circuitry associated with exercise. Observations of changes in heart rate (HR) and in cortical blood oxygenation level-dependent (BOLD) images were made in 23 control subjects [control; 8 women; 63 ± 11 yr; mean arterial pressure (MAP): 90 ± 9 mmHg] (mean ± SD) and 17 similarly aged CAD patients (4 women; 59 ± 9 yr; MAP: 87 ± 10 mmHg). Four repeated bouts each of 30%, 40%, and 50% of maximal voluntary contraction (MVC) force (LAB session), and seven repeated bouts of isometric handgrip (IHG) at 40% MVC force (fMRI session), were performed, with each contraction lasting 20 s and separated by 40 s of rest. There was a main effect of group (P = 0.03) on HR responses across all IHG intensities. Compared with control, CAD demonstrated less task-dependent deactivation in the posterior cingulate cortex and medial prefrontal cortex, and reduced activation in the right anterior insula, bilateral precentral cortex, and occipital lobe (P < 0.05). When correlated with HR, CAD demonstrated reduced activation in the bilateral insula and posterior cingulate cortex, and reduced deactivation in the dorsal anterior cingulate cortex, and bilateral precentral cortex (P < 0.05). The increased variability in expected autonomic regions and decrease in total cortical activation in response to the IHG task are associated with a diminished HR response to volitional effort in CAD. Therefore, relative to similarly aged and healthy individuals, CAD impairs the heart rate response and modifies the cortical patterns associated with cardiovascular control during IHG.
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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 | 0.000 |
| 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) | 0.001 | 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".