An investigation of changes in regional gray matter volume in cardiovascular disease patients, pre and post cardiovascular rehabilitation
Bibliographic record
Abstract
Cognitive function decline secondary to cardiovascular disease has been reported. However, little is known about the impact of coronary artery disease (CAD) on the aging brain macrostructure or whether exercise training, in the context of cardiovascular rehabilitation, can affect brain structure following a coronary event. This study employed voxel-based morphometry of high resolution structural MRI images to investigate; 1) changes in regional gray matter volume (GMV) in CAD patients compared to age-matched controls, and 2) the effects of a six-month exercise-based cardiovascular rehabilitation program on CAD-related GMV decline. Compared to controls, significant decreases in regional GMV were found in the superior, medial and inferior frontal gyrus; superior and inferior parietal gyrus; middle and superior temporal gyrus and in the posterior cerebellum of CAD patients. Cardiovascular rehabilitation was associated with the recovery of regional GMV in the superior frontal gyrus, superior temporal gyrus and posterior cerebellum of the CAD patients as well as the increase in GMV in the supplementary motor area. Total and regional GMV correlated with fitness level, defined by the maximal oxygen consumption (VO2max), at baseline but not after cardiovascular rehabilitation. This study demonstrates that cardiovascular disease can adversely affect age-related decline in GMV; and that these disease-related effects could be mitigated by moderate levels of exercise training as part of cardiovascular rehabilitation.
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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 | 0.001 | 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".