Reconstruction of respiratory variation signals from fMRI data

• Main Authors: Jorge A. Salas, Roza G. Bayrak, Yuankai Huo, Catie Chang
• Format: Article
• Language: English
• Published: Elsevier 2021-01-01
• Series: NeuroImage
• Subjects: Respiratory variation; fMRI; Physiology; Resting state; Machine learning
• Online Access: http://www.sciencedirect.com/science/article/pii/S1053811920309447

Functional MRI signals can be heavily influenced by systemic physiological processes in addition to local neural activity. For example, widespread hemodynamic fluctuations across the brain have been found to correlate with natural, low-frequency variations in the depth and rate of breathing over time. Acquiring peripheral measures of respiration during fMRI scanning not only allows for modeling such effects in fMRI analysis, but also provides valuable information for interrogating brain-body physiology. However, physiological recordings are frequently unavailable or have insufficient quality. Here, we propose a computational technique for reconstructing continuous low-frequency respiration volume (RV) fluctuations from fMRI data alone. We evaluate the performance of this approach across different fMRI preprocessing strategies. Further, we demonstrate that the predicted RV signals can account for similar patterns of temporal variation in resting-state fMRI data compared to measured RV fluctuations. These findings indicate that fluctuations in respiration volume can be extracted from fMRI alone, in the common scenario of missing or corrupted respiration recordings. The results have implications for enriching a large volume of existing fMRI datasets through retrospective addition of respiratory variations information.