Characteristics of respiratory measures in young adults scanned at rest, including systematic changes and "missed" respiratory events

Breathing rate and depth influence the concentration of carbon dioxide in the blood, altering cerebral blood flow and thus functional magnetic resonance imaging (fMRI) signals. Such respiratory fluctuations can have substantial influence in studies of fMRI signal covariance in subjects at rest, the so-called resting state functional connectivity technique. If respiration is monitored during fMRI scanning, it is typically done using a belt about the subjects abdomen to record abdominal circumference. Several measures have been derived from these belt records, including the windowed envelope of the waveform (ENV), the windowed variance in the waveform (respiration variation, RV), and a measure of the amplitude of each breath divided by the cycle time of the breath (respiration volume per time, RVT). Any attempt to gauge respiratory contributions to fMRI signals requires a respiratory measure, but little is known about how these measures compare to each other, or how they perform beyond the small studies in which they were initially proposed. In this paper, we examine the properties of these measures in hundreds of healthy young adults scanned for an hour each at rest, a subset of the Human Connectome Project chosen for having high-quality physiological records. We find: 1) ENV, RV, and RVT are all similar, though ENV and RV are more similar to each other than to RVT; 2) respiratory events like deep breaths exhibit characteristic fMRI signal changes, head motions, and image quality abnormalities time-locked to deep breaths evident in the belt traces; 3) all measures can miss respiratory events evident in the belt traces; 4) RVT misses deep breaths (i.e., yawns and sighs) more than ENV or RV; 5) all respiratory measures change systematically over the course of a 14.4-minute scan, decreasing in mean value. We discuss the implication of these findings for the literature, and ways to move forward in modeling respiratory influences on fMRI scans.