Modelling the effect of cardiac and respiratory fluctuations on the central autonomic network in a novel test-retest dataset
Bibliographic record
Abstract
Changes in physiological state corresponding to fluctuations in heart rate and respiration drive non-neuronal contributions to the BOLD fMRI signal, complicating investigation of regions of the brain which participate in and process autonomic regulation: the central autonomic network (CAN). The estimation of physiological response functions (PRFs) provides a tool to interrogate and minimize the effects of these noise processes on fMRI connectivity. In this study, we explore the reproducibility of cardiac and respiratory response functions used to denoise resting and task data acquired with 3T MRI and their effect on the test-retest reliability of connectivity within the CAN. We characterize group-level PRFs during rest, fast-paced breathing and breath-holds, and a cold-pressor task and show that cardiac response dynamics vary significantly across scan conditions and subjects. Comparing physiological nuisance signals with indices of sympathetic and parasympathetic activity used to map the CAN, we further demonstrate that PRFs may provide an opportunity to disentangle neuronal and non-neuronal correlates of cardiac activity in fMRI data. Finally, we evaluate the effect of denoising on the test-retest reliability of connectivity between regions associated with the CAN, shedding light on the uses and limitations of PRFs for fMRI studies of brain-body interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".