Brain-heart interactions in novice meditation practitioners during breath focus and an arithmetic task
Bibliographic record
Abstract
ABSTRACT Objectives The study of neural and visceral oscillatory activities reveals that both subsystems and their interactions influence human cognition. In particular, cardiac and neural changes during self-regulation processes can be studied through a comparison of stress-inducing procedures and meditation practices. Methods In this study, we investigate the characteristic profiles of neural-cardiac interactions during a stress-inducing arithmetic task and a breath focus meditation period in a sample of 21 young participants (10 women, age range 20-29) with no prior experience in meditation practices. Using recordings of electroencephalography (EEG) and electrocardiography (ECG), we assessed instantaneous cross-frequency relationships between the alpha neural band and heart rate in both conditions. Results Our results indicate significant heart rate and alpha frequency decelerations during breath focus compared to the stress-inducing task. Regarding alpha: heart rate cross-frequency relationships, the stress-inducing arithmetic task exhibited ratios of smaller magnitude than the breath focus task, including a higher incidence of the specific 8:1 cross-frequency relationship, compared to the breath-focus task, proposed to enable cross-frequency coupling among neural and cardiac rhythms during mild cognitive stress. The change in cross-frequency relationships were mostly driven by changes in heart rate frequency between the two tasks, as indicated through surrogate data analyses. Conclusions Our results provide novel evidence that stress responses and changes during meditation practices can be better characterized by integrating physiological markers and, more crucially, their interactions. Together, this physiologically comprehensive approach can aid in guiding interventions such as physiology modulation protocols (biofeedback and neurofeedback) for emotion and stress-regulation.
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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 | 0.000 | 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) | 0.002 | 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".