Generating dynamic carbon-dioxide traces from respiration-belt recordings: Feasibility using neural networks and application in functional magnetic resonance imaging.

Introduction: In the context of functional magnetic resonance imaging (fMRI), carbon dioxide (CO₂) is a well-known vasodilator that has been widely used to monitor and interrogate vascular physiology. Moreover, spontaneous fluctuations in end-tidal carbon dioxide (PETCO₂) reflects changes in arterial CO₂ and has been demonstrated as the largest physiological noise source for denoising the low-frequency range of the resting-state fMRI (rs-fMRI) signal. However, the majority of rs-fMRI studies do not involve CO₂ recordings, and most often only heart rate and respiration are recorded. While the intrinsic link between these latter metrics and CO₂ led to suggested possible analytical models, they have not been widely applied.

Methods: In this proof-of-concept study, we propose a deep-learning (DL) approach to reconstruct CO2 and PETCO2 data from respiration waveforms in the resting state.

Results: We demonstrate that the one-to-one mapping between respiration and CO₂ 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 CO₂. Moreover, dynamic PETCO₂ can be successfully derived from the predicted CO₂, 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 CO₂ prediction.

Discussion: Our results demonstrate that dynamic CO₂ can be obtained from respiration-volume using neural networks, complementing the still few reports in DL of physiological fMRI signals, and paving the way for further research in DL based bio-signal processing.