Effect of a simulated functional magnetic resonance imaging (fMRI) scanner on resting and exercise‐induced changes in cardiovascular (CV) function
Bibliographic record
Abstract
fMRI scanning confounds real‐time recordings of brain neural and CV recordings and can produce claustrophobia‐induced anxiety. As such, separate data collection sessions are often required and the assumption made that resting and reflex‐mediated changes in CV function are consistent. This study determined if a simulated fMRI scanner affects cardiovascular function at rest, during isometric handgrip exercise (IHG) and a period of post‐exercise ischemia (PEI). On separate days, 9 healthy participants (4M/5F, 21 ± 1 years) had heart rate (HR), mean arterial pressure (MAP), and calf blood flow (CBF) measured while supine on a standard lab bed or a simulated fMRI scanner. After ~10 min of rest they performed a 90s IHG at 40% maximum force followed by 2 min of PEI (no exercise). Heart rate variability indices (HRV) and calf vascular resistance (CVR) were calculated to estimate autonomic innervation to the heart and blood vessels. At rest, CBF was lower and CVR higher (both, p<0.05) in the simulated fMRI scanner with no differences in resting HR, MAP, or HRV. No differences in the CV responses to IHG or PEI were observed between conditions. These data suggest that the fMRI environment increased resting sympathetic vasoconstriction to the calf but did not influence the CV response to IHG or PEI.
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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 | 0.001 |
| 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".