Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal

Abstract

Heart rate fluctuations occur in the low frequency region (< 0.1 Hz) probed in functional magnetic resonance imaging (fMRI) studies of resting-state functional connectivity and most fMRI block paradigms, and may be related to low frequency blood-oxygenation-level-dependent (BOLD) signal fluctuations. To investigate this hypothesis, temporal correlations between cardiac rate and restingstate fMRI signal timecourses were assessed at 3 Tesla. Resting-state BOLD fMRI and accompanying physiological data were acquired and analyzed using cross-correlation and regression. Time-shifted cardiac rate timecourses were included as regressors in addition to established physiological regressors (RETROICOR (Glover et al., 2000) and respiration volume per unit time (Birn et al., 2006b)). Significant correlations between the cardiac rate and BOLD signal timecourses were revealed, particularly negative correlations in gray matter at time-shifts of 6-12 seconds and positive correlations at time shifts of 30-42 seconds (TR = 6 s). Regressors consisting of cardiac rate timecourses shifted by delays of between 0 and 24 seconds explained an additional 1 % of the BOLD signal variance on average over the whole brain across 9 subjects, a similar additional variance to that explained by respiration volume per unit time and RETROICOR regressors, even when used in combination with these other physiological regressors. This suggests that including such time-shifted cardiac rate regressors will be beneficial for explaining physiological noise variance and will thereby improve the statistical power in future task-based and resting-state fMRI studies.

Physiological noise in human cerebellar fMRI

“…This significant increase (80 %, t test p < 0.05) of respiratory EV for the cerebellum, relative to the superior ROIs was found in the motor run, while intra-subject differences in the rest run meant no significant differences were found between ROIs for the respiratory RETROICOR components. The CR regressors explained approximately 1 % of variance, comparable to previously reported values [34]. The RVT regressor explained approximately 1 % of variance in the rest run for all ROIs, but less in the motor runs, ranging from 0.2 % in SMA to 0.9 % in CVIII.…”

“…No temporal highpass or low-pass smoothing was applied. RETROICOR regressors up to second order were computed following [17], and additional regressors were added for the respiratory volume per unit of time (RVT) [32,33] and the cardiac rate (CR) [34,35]. To measure the amount of data variation explained by each group of regressors, effectively comparing GLM models of different sizes, R 2 adj values [34] were computed on a voxel-by-voxel basis following Eq.…”

Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

“…• Cardiac-rate variability (CRV): Variability in heart rate has also been found to strongly modulate the rs-fMRI signal (Chang et al, 2009a;Shmueli et al, 2007), not only near the cerebrospinal fluid and major blood vessels, but throughout the grey matter. It is well established that respiration and cardiac pulsation are related through respiratory sinus arrhythmia, a naturally occurring variation in heart rate during a breathing cycle driven by the parasympathetic nervous system.…”

Influence of physiological noise on accelerated 2D and 3D resting state functional MRI data at 7 T

“…Over the years, a large number of mechanisms have been identified as sources of physiological noise, including cardiac and respiratory cycles and depth rates. RETROICOR models (22) have been proposed to account for the former, namely phase-related contributions, whereas the latter can be modeled using cardiac rate (CR) (23)(24)(25) and respiratory volume per unit time (RVT) models (26,27). At high fields, a combination of these various regressors has been reported to improve the temporal SNR (tSNR) (19,21,28), a quantitative indicator of fMRI detection capabilities.…”