Sackler Institute for Developmental Psychobiology, Department of Psychiatry, Weill Cornell Medicine, 1300 York Avenue, Box 140, New York, NY, 10065, USA.
Brain and Mind Research Institute, Weill Cornell Medicine, 1300 York Avenue, Box 140, New York, NY, 10065, USA.
Neuroimage. 2020 Jan 1;204:116234. doi: 10.1016/j.neuroimage.2019.116234. Epub 2019 Oct 5.
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 subject's 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. Here, 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 correlated, and ENV and RV are more highly correlated to each other than to RVT; 2) respiratory events like deep breaths exhibit characteristic heart rate elevations, fMRI signal changes, head motions, and image quality abnormalities time-locked to large deflections in the belt traces; 3) all measures can "miss" deep breaths; 4) RVT "misses" deep breaths more than ENV or RV; 5) all respiratory measures change systematically over the course of a 14.4-min scan. We discuss the implications of these findings for the literature and ways to move forward in modeling respiratory influences on fMRI scans.
呼吸频率和深度会影响血液中二氧化碳的浓度,从而改变脑血流,进而影响功能磁共振成像(fMRI)信号。在对处于静息状态的被试的 fMRI 信号协方差进行研究时,这种呼吸波动会产生实质性的影响,这种技术被称为“静息态功能连接”。如果在 fMRI 扫描期间监测呼吸,通常会使用一条环绕被试腹部的腰带来记录腹围。从这些腰带记录中可以得出几个指标,包括波形的窗口包络(ENV)、波形的窗口方差(呼吸变化,RV)以及每个呼吸幅度除以呼吸周期时间的指标(呼吸时间比,RVT)。任何评估呼吸对 fMRI 信号贡献的尝试都需要一个呼吸指标,但对于这些指标彼此之间的比较,以及它们在最初提出的小型研究之外的表现,人们知之甚少。在这里,我们在数百名健康的年轻成年人中进行了检查,这些成年人在休息时被扫描了一个小时,这是人类连接组计划的一个子集,选择他们是因为他们有高质量的生理记录。我们发现:1)ENV、RV 和 RVT 均相关,且 ENV 和 RV 彼此之间的相关性高于与 RVT 的相关性;2)深呼吸等呼吸事件会出现特征性的心率升高、 fMRI 信号变化、头部运动以及与腰带轨迹大偏移时间锁定的图像质量异常;3)所有指标都可能“错过”深呼吸;4)RVT“错过”深呼吸的次数多于 ENV 或 RV;5)所有呼吸指标在 14.4 分钟的扫描过程中会系统性地发生变化。我们讨论了这些发现对文献的影响,以及在对 fMRI 扫描中呼吸影响进行建模方面的前进方向。