Influence of heart rate on the BOLD signal: The cardiac response function

Department of Electrical Engineering, Stanford University, Lucas MRI/S Center, Stanford, CA 94305-5488, USA.
NeuroImage (Impact Factor: 6.36). 11/2008; 44(3):857-69. DOI: 10.1016/j.neuroimage.2008.09.029
Source: PubMed


It has previously been shown that low-frequency fluctuations in both respiratory volume and cardiac rate can induce changes in the blood-oxygen level dependent (BOLD) signal. Such physiological noise can obscure the detection of neural activation using fMRI, and it is therefore important to model and remove the effects of this noise. While a hemodynamic response function relating respiratory variation (RV) and the BOLD signal has been described [Birn, R.M., Smith, M.A., Jones, T.B., Bandettini, P.A., 2008b. The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration. Neuroimage 40, 644-654.], no such mapping for heart rate (HR) has been proposed. In the current study, the effects of RV and HR are simultaneously deconvolved from resting state fMRI. It is demonstrated that a convolution model including RV and HR can explain significantly more variance in gray matter BOLD signal than a model that includes RV alone, and an average HR response function is proposed that well characterizes our subject population. It is observed that the voxel-wise morphology of the deconvolved RV responses is preserved when HR is included in the model, and that its form is adequately modeled by Birn et al.'s previously-described respiration response function. Furthermore, it is shown that modeling out RV and HR can significantly alter functional connectivity maps of the default-mode network.

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    • "In general, correlations are known to be systematically biased downward due to measurement instability, a phenomenon termed attenuation [Fan, 2003]. fcMRI estimates are therefore likely to be influenced by measurement instability of the BOLD signal that arises from technical constraints like magnetic susceptibility artifacts [Ojemann et al., 1997] as well as physiological functions such as respiration and cardiac activity [Birn et al., 2008; Chang et al., 2009]. Other sources of instability may arise from neurally meaningful variations in network dynamics and configurations [Cole et al., 2013; Hutchison et al., 2013; Krienen et al., 2014; Shirer et al., 2012]. "
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    ABSTRACT: Network properties can be estimated using functional connectivity MRI (fcMRI). However, regional variation of the fMRI signal causes systematic biases in network estimates including correlation attenuation in regions of low measurement reliability. Here we computed the spatial distribution of fcMRI reliability using longitudinal fcMRI datasets and demonstrated how pre-estimated reliability maps can correct for correlation attenuation. As a test case of reliability-based attenuation correction we estimated properties of the default network, where reliability was significantly lower than average in the medial temporal lobe and higher in the posterior medial cortex, heterogeneity that impacts estimation of the network. Accounting for this bias using attenuation correction revealed that the medial temporal lobe's contribution to the default network is typically underestimated. To render this approach useful to a greater number of datasets, we demonstrate that test-retest reliability maps derived from repeated runs within a single scanning session can be used as a surrogate for multi-session reliability mapping. Using data segments with different scan lengths between 1 and 30 min, we found that test-retest reliability of connectivity estimates increases with scan length while the spatial distribution of reliability is relatively stable even at short scan lengths. Finally, analyses of tertiary data revealed that reliability distribution is influenced by age, neuropsychiatric status and scanner type, suggesting that reliability correction may be especially important when studying between-group differences. Collectively, these results illustrate that reliability-based attenuation correction is an easily implemented strategy that mitigates certain features of fMRI signal nonuniformity. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.
    Human Brain Mapping 08/2015; DOI:10.1002/hbm.22947 · 5.97 Impact Factor
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    • "The first 10 frames of each scan were discarded to allow the MR signal to achieve T1 equilibration. Standard preprocessing included slice-time correction, physiological noise correction with both RETROICOR (Glover et al., 2000) and RVHRCOR (Chang et al., 2009), and nuisance regression of scan drifts (linear and quadratic trends), six head motion parameters, signals extracted from white matter and CSF (3-mm radius spheres centered at MNI (26, − 12, 35) and (19, − 33, 18), renormalized to each subject's native space using SPM5 (Wellcome Department of Cognitive Neurology, London)). Subjects' behavioral time series (reaction time sequence convolved with the hemodynamic response function) (Chen et al., 2013) were also regressed out of the WM task data. "
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    ABSTRACT: Recently, fMRI researchers have begun to realize that the brain's intrinsic network patterns may undergo substantial changes during a single resting state (RS) scan. However, despite the growing interest in brain dynamics, metrics that can quantify the variability of network patterns are still quite limited. Here, we first introduce various quantification metrics based on the extension of co-activation pattern (CAP) analysis, a recently proposed point-process analysis that tracks state alternations at each individual time frame and relies on very few assumptions; then apply these proposed metrics to quantify changes of brain dynamics during a sustained 2-back working memory (WM) task compared to rest. We focus on the functional connectivity of two prominent RS networks, the default-mode network (DMN) and executive control network (ECN). We first demonstrate less variability of global Pearson correlations with respect to the two chosen networks using a sliding-window approach during WM task compared to rest; then we show that the macroscopic decrease in variations in correlations during a WM task is also well characterized by the combined effect of a reduced number of dominant CAPs, increased spatial consistency across CAPs, and increased fractional contributions of a few dominant CAPs. These CAP metrics may provide alternative and more straightforward quantitative means of characterizing brain network dynamics than time-windowed correlation analyses. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 02/2015; 111. DOI:10.1016/j.neuroimage.2015.01.057 · 6.36 Impact Factor
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    • "Another potential source for non-BOLD functional connectivity relates to the physiological noise, which has been demonstrated by Kruger and Glover (2001) to contain both BOLD-like components (caused by the same mechanism that induces changes in R 2 ⁎ ) and non- BOLD-like image-to-image signal fluctuations due to cardiac/respiratory functions. Although physiological corrections (Glover et al., 2000; Chang et al., 2009) were applied to the datasets, residuals may still persist and contribute to the non-TE dependency of the RS functional connectivity. There are two arguments against this hypothesis: (1) as Fig. 7. (A) Correlated signal amplitudes (averaged across ROI signal pairs) and noise amplitudes (root mean square of the uncorrelated residuals, also estimated from ROI signal pairs) at different frequency bands; each dot represents the result from a single scan (scans with TE = 25 and 30 ms are displayed). "
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    ABSTRACT: Blood oxygen level dependent (BOLD) spontaneous signals from resting-state (RS) brains have typically been characterized by low-pass filtered timeseries at frequencies≤0.1Hz, and studies of these low-frequency fluctuations have contributed exceptional understanding of the baseline functions of our brain. Very recently, emerging evidence has demonstrated that spontaneous activities may persist in higher frequency bands (even up to 0.8Hz), while presenting less variable network patterns across the scan duration. However, as an indirect measure of neuronal activity, BOLD signal results from an inherently slow hemodynamic process, which in fact might be too slow to accommodate the observed high-frequency functional connectivity (FC). To examine whether the observed high-frequency spontaneous FC originates from BOLD contrast, we collected RS data as a function of echo time (TE). Here we focus on two specific resting state networks - the default-mode network (DMN) and executive control network (ECN), and the major findings are fourfold: (1) we observed BOLD-like linear TE-dependence in the spontaneous activity at frequency bands up to 0.5Hz (the maximum frequency that can be resolved with TR=1s), supporting neural relevance of the RSFC at higher frequency range; (2) Conventional models of hemodynamic response functions must be modified to support resting state BOLD contrast, especially at higher frequencies; (3) there are increased fractions of non-BOLD-like contributions to the RSFC above the conventional 0.1Hz (non-BOLD/BOLD contrast at 0.4~0.5Hz is~4 times that at <0.1Hz); and (4) the spatial patterns of RSFC are frequency-dependent. Possible mechanisms underlying the present findings and technical concerns regarding RSFC above 0.1Hz are discussed. Copyright © 2014. Published by Elsevier Inc.
    NeuroImage 12/2014; 107. DOI:10.1016/j.neuroimage.2014.12.012 · 6.36 Impact Factor
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