Limitations Of the Derived Respiratory Variation Measurements Used in Functional Magnetic Resonance Imaging
Bibliographic record
Abstract
Among different physiological sources of noise in blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-fMRI), low-frequency fluctuation in arterial carbon dioxide (CO2) constitutes the strongest modulator of the BOLD signal. In this paper, the performance of respiration variation (RV) and respiratory volume per time (RVT) in identifying abnormal but prominent respiratory patterns are studied. We used Human Connectome Project in the Developmental dataset, as children are a challenging cohort in fMRI studies and have irregular breathing. According to our findings, there is no guarantee that a given respiratory event evident in the abdominal respiratory belt transducer timeseries, such as a deep breath or pause in breathing, will be detectable in both RVT and RV. In addition, RVT and RV do not show similar behavior during some respiratory events, especially when the subject breathes deeply at a low rate, while they use almost similar respiratory response functions.
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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 | 0.015 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".