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.

Introduction

It is important to identify and characterize the sources of physiological noise in the blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal so that noise reduction strategies can be developed and improved. This is especially relevant as technical advances, such as the use of higher magnetic fields or multiple receiver coils, have increased the available MRI signal. Physiological noise depends on the total signal strength and therefore becomes a larger fraction of the total noise as the signal increases (Kruger and Glover, 2001, Kruger et al., 2001, Triantafyllou et al., 2005), for example with increased magnetic field strength.

Physiological noise negatively affects conventional task-based fMRI experiments and has been found to be particularly problematic in the absence of external stimuli, in resting-state functional connectivity analysis (Cordes et al., 2001). In this type of analysis neuronal connections between brain regions are inferred by measuring the spatial correlations of low-frequency (< 0.1 Hz) BOLD fMRI signal fluctuations in the brain. The minimization of physiological noise as a confound in such studies has been a preoccupation since their inception (Biswal et al., 1996, Fukunaga et al., 2006, Lowe and Sakaie, 2006, Vogt et al., 2006).

Several physiological noise sources have been identified, including effects related to the cardiac (Bhattacharyya and Lowe, 2004, Dagli et al., 1999) and respiratory (Birn et al., 2006b, Wise et al., 2004) cycles. Variations related to arterial carbon dioxide fluctuations (Wise et al., 2004) and residual movement artifacts not accounted for by rigid body registration (Lund et al., 2005) also contribute.

Various methods for reducing physiological noise have been proposed. These may operate in k-space (Hu et al., 1995, Le and Hu, 1996, Wowk et al., 1997) or in image space (Chuang and Chen, 2001, Deckers et al., 2006, Glover et al., 2000) with the latter being the preferred method since changes made in k-space affect all the voxels in the reconstructed images. This makes spatially localized noise difficult to remove and may induce spatial correlations. Fixed bandwidth band-reject filtering was initially proposed (Biswal et al., 1996) to remove noise at the fundamental cardiac and respiratory frequencies but can only be applied successfully when variations at these frequencies are stationary and adequately sampled (as in the rare case of imaging with very short TR) so that they are not aliased to lower frequencies. Moreover, such filtering cannot be applied if task-related signals are present in the rejected frequency band.

Some noise reduction methods involve the acquisition of additional physiological data (Glover et al., 2000, Hu et al., 1995, Liston et al., 2006, Vogt et al., 2006) using a photoplethysmograph and pneumatic belt, for example. Other methods utilize the MRI data itself to estimate the noise (Le and Hu, 1996, Lowe and Sakaie, 2006, Wowk et al., 1997). Some of the methods are designed for straightforward data correction (Glover et al., 2000) but most can be extended to perform ‘nuisance variable regression’ (Birn et al., 2006a, Lund et al., 2006) in which the physiological noise measures (or models derived from them) are included as regressors in a general linear model (GLM) regression analysis. This regression method is advantageous over applying a filter or additional data correction as it does not interfere with the detection of functional activation (Deckers et al., 2006).

Many studies have demonstrated that there are fMRI signal changes associated with the pulsatile cardiac motion (both at the cardiac frequency and its harmonics) (Bhattacharyya and Lowe, 2004, Biswal et al., 1996, Dagli et al., 1999, Kruger and Glover, 2001). Most of the physiological noise reduction methods model the contribution from the cardiac motion by determining the relative timing of each image volume (or slice (Liston et al., 2006, Vogt et al., 2006)) within the cardiac cycle. They assume that the cardiac cycle follows the same inherent pattern regardless of the inter-beat time interval. This assumption is not entirely accurate as there are differences in the time interval between beats that are associated more with time variation in the diastolic portion of the cardiac cycle than the systolic (Dagli et al., 1999).

Across-beat fluctuations in the heart rate have been found to occur in several frequency bands (Akselrod et al., 1981, Cohen and Taylor, 2002, Otzenberger et al., 1998) including the low-frequency region investigated in fMRI studies of resting-state functional connectivity (< 0.1 Hz). In fact, a recent study suggests that systemic cardiovascular fluctuations (low-frequency oscillations in the heart rate and arterial blood pressure) can account for about half of the information carried with low-frequency oscillations in the cerebral hemodynamics (specifically in the oxyhemoglobin concentration change) (Katura et al., 2006). Fluctuations in the heart rate may therefore be related to those in the BOLD fMRI signal, particularly since the signal is dependent on the cerebral blood flow, oxygenation and volume. Furthermore, since respiration and the cardiac rate are intimately related (Cohen and Taylor, 2002) and the respiration volume per unit time (RVT) has recently been found to be correlated with the resting-state fMRI BOLD signal (Birn et al., 2006b), it is probable that fluctuations in the cardiac rate are related to those in the resting-state fMRI BOLD signal. The effect of cardiac rate fluctuations on the fMRI signal has not been assessed or explicitly accounted for in physiological noise models.

Therefore, the aim of this study was to investigate the hypothesis that across-beat fluctuations in the cardiac rate are correlated with low-frequency variations in the resting-state BOLD fMRI signal. Temporal and spatial patterns of fMRI signal changes related to variations in the cardiac rate were characterized. To this end, resting-state BOLD fMRI data and accompanying physiological data were acquired and analyzed using both cross-correlation and regression. Subsequent to and informed by the cross-correlation analysis, several nested regression analyses were performed to determine whether adding a set of time-shifted cardiac rate timecourses to other established physiological noise regressors would explain additional variance in the data.