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 range (<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 resting-state fMRI signal timecourses were assessed at 3 T. 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, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn Reson Med 44, 162-167) and respiration volume per unit time (Birn, R.M., Diamond, J.B., Smith, M.A., Bandettini, P.A., 2006b. Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage 31, 1536-1548). Significant correlations between the cardiac rate and BOLD signal timecourses were revealed, particularly negative correlations in gray matter at time shifts of 6-12 s and positive correlations at time shifts of 30-42 s (TR=6 s). Regressors consisting of cardiac rate timecourses shifted by delays of between 0 and 24 s 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.

Figure 1

An illustration of the processing steps in the extraction of the cardiac rate from the TTL pulse timecourse. The initial estimate of the heart rate from the inverse of the TTL pulse intervals is shown schematically in a. Figure b shows the removal of spurious heart rate timepoints from the whole cardiac rate timecourse. The effect of smoothing the cardiac rate timecourse with the Gaussian kernel is shown over a section of the timecourse in c and the effect smoothing on the spectrum of cardiac rate variations is shown in d. Figure e shows (over a small section of the cardiac rate timecourse) the final resampling so that there is one timepoint for every MRI image volume or 6 s TR period. The cardiac rate timecourse shown in b-e is that measured for the subject that appears in Figures 3, 4, 6, 7 and 9.

Figure 2

The mean Fourier transform of the smoothed and resampled cardiac rate timecourse is plotted (prior to polynomial detrending and filtering). The Fourier-transformed data were averaged over nine subjects (excluding repeat sessions) and the error bars, displayed at every fourth data point for clarity, indicate (±) the standard deviation over the nine subjects.

Figure 3

T-maps of the correlation between the measured (a) or simulated (b) cardiac rate and resting-state fMRI signal timecourses in one slice of a single subject for lags -10 to +10 TR (± 1 minute). The t-values above the threshold are shown overlaid on the first raw EPI image. The t-maps and images have been masked to exclude areas outside the brain. There is a small area of correlation that appears outside the brain where the skull-stripping failed to remove subcutaneous tissue.

Figure 4

A single-subject map of t-values of the correlation of the resting-state fMRI signal timecourse with the measured (a) or simulated (b) cardiac rate timecourse shifted by 2 TR. The map is overlaid on the first raw EPI image and both have been masked to exclude areas outside the brain. There is a small area of correlation that appears outside the brain where the skull-stripping failed to remove subcutaneous tissue.

Figure 5

The mean t-values of correlation between signal timecourses in the gray matter (black) and white matter (red) and the measured cardiac rate shifted over a range of lag times. Data for two scanning sessions from the same subject are shown in a) and b). The mean t-value in the gray matter is shown for each subject in c) and averaged across all subjects (with error bars equal to the standard error of the mean) in d). The stars in d) indicate those average t-values that were found in a further t-test to be significantly different from zero (two-tailed, p <0.05). The t-values of correlation with the simulated cardiac rate are shown in e) for the same scanning session as in b). For ease of comparison, the same scale is used in Figures 5 a, b, c and e.

Figure 6

The maximum (positive or negative) correlation coefficient in each voxel in the brain between delays of -10 and + 10 TR. The data are for the same subject as in Figures 3-4.

Figure 7

Lag histograms showing the lags between ± 10 TR at which voxels in the brain have the largest correlation coefficients between the measured (a) or simulated (b) cardiac rate timecourse and the resting-state BOLD signal. Figure 7 a shows the same data as in Figure 6 for the same subject as in Figures 3-4.

Figure 8

Maps of group t-statistics of the correlation between the measured cardiac rate and MRI signal timecourses. The top row shows the statistics for lags of 1 and 2 TR (6 and 12 s) combined (maximum t over the two lags) and the bottom row shows the statistics for lags of 5 and 6 TR (30 and 36 s) combined (maximum t over the two lags). These pairs of lags were chosen since they had the most significant correlations in the gray matter (see Figure 4d). The t-statistics above a threshold of 3.09 (p < 0.001) are shown overlaid on the group averaged EPI image (MNI space). The t-maps were masked to exclude areas outside the brain.

Figure 9

Maps of the successive differences in ${\mathrm{R}}_{\mathrm{adj}}^2\hspace{0.17em}(\mathrm{\Delta }{\mathrm{R}}_{\mathrm{adj}}^2)$ between the nested regression models are shown to illustrate the effect of adding in each set of regressors. The differences displayed are $\mathrm{\Delta }{\mathrm{R}}_{\mathrm{adj},2-1}^2(\mathrm{a})$, $\mathrm{\Delta }{\mathrm{R}}_{\mathrm{adj},3-2}^2(\mathrm{b})$, ${\mathrm{R}}_{\mathrm{adj},4-2}^2(\mathrm{c})$, $\mathrm{\Delta }{\mathrm{R}}_{\mathrm{adj},5-4}^2(\mathrm{d})$, $\mathrm{\Delta }{\mathrm{R}}_{\mathrm{adj},5-3}^2(\mathrm{e})$ and $\mathrm{\Delta }{\mathrm{R}}_{\mathrm{adj},5-1}^2(\mathrm{f})$. The maps are scaled between 0 (black) and 0.1 (white) except for the final map in (f) which is scaled between 0 (black) and 0.2 (white). These results are for the same subject as shown in Figures 3, 4, 6 and 7.