From Brain to Body: Learning Low-Frequency Respiration and Cardiac Signals from fMRI Dynamics

Abstract

Functional magnetic resonance imaging (fMRI) is a powerful technique for studying human brain activity and large-scale neural circuits. However, fMRI signals can be strongly modulated by slow changes in respiration volume (RV) and heart rate (HR). Monitoring cardiac and respiratory signals during fMRI enables modeling and/or reducing such effects; yet, physiological measurements are often unavailable in practice, and are missing from a large number of fMRI datasets. Very recent work has demonstrated the ability to reconstruct RV signals from resting-state fMRI data, but it is currently unclear whether such an approach generalizes to other physiological signals (such as HR) or across fMRI task conditions. Here, we propose a joint learning approach for inferring RV and HR signals directly from fMRI time-series dynamics. Our models are trained on resting-state fMRI data using the largest dataset employed for the problem, and tested both on resting-state fMRI and on separate fMRI paradigms that were acquired during three task conditions: emotion processing, social cognition, and working memory. We demonstrate that our deep LSTM model successfully captures both RV and HR patterns, outperforming existing approaches, and translates to scans of variable lengths and different experimental conditions. Source code is available at: https://github.com/neurdylab/deep-physio-recon.