Generating dynamic carbon-dioxide from the respiratory-volume time series: A feasibility study using neural networks
Bibliographic record
Abstract
Abstract In the context of fMRI, carbon dioxide (CO 2 ) is a well-known vasodilator that has been widely used to monitor and interrogate vascular physiology. Moreover, spontaneous fluctuations in end-tidal carbon dioxide (PETCO 2 ) reflects changes in arterial CO 2 and has been demonstrated as the largest physiological noise source in the low-frequency range of the resting-state fMRI (rs-fMRI) signal. Increasing appreciation for the role of CO 2 in fMRI has given rise to methods that use it for physiological denoising or estimating cerebrovascular reactivity. However, the majority of rs-fMRI studies do not involve CO 2 recordings, and most often only heart rate and respiration are recorded. While the intrinsic link between these latter metrics and CO 2 led to suggested possible analytical models, they have not been widely applied. In this proof-of-concept study, we propose a deep learning approach to reconstruct CO 2 and PETCO 2 data from respiration waveforms in the resting state. We demonstrate that the one-to-one mapping between respiration and CO 2 recordings can be well predicted using fully convolutional networks (FCNs), achieving a Pearson correlation coefficient (r) of 0.946 ± 0.056 with the ground truth CO 2 . Moreover, dynamic PETCO 2 can be successfully derived from the predicted CO 2 , achieving r of 0.512 ± 0.269 with the ground truth. Importantly, the FCN-based methods outperform previously proposed analytical methods. In addition, we provide guidelines for quality assurance of respiration recordings for the purposes of CO 2 prediction. Our results demonstrate that dynamic CO 2 can be obtained from respiration-volume using neural networks, complementing the still few reports in deep-learning of physiological fMRI signals, and paving the way for further research in deep-learning based bio-signal processing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".