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 (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 for denoising the low-frequency range of the resting-state fMRI (rs-fMRI) signal. 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. 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 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. DISCUSSION: Our results demonstrate that dynamic CO(2) 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.