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

By Catie Chang, John P. Cunningham, Gary H. Glover. NeuroImage, 2008

689 citations recorded by the source corpus.

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 its effects. While a hemodynamic response function relating respiratory variation (RV) and the BOLD signal has been described (Birn et al., 2008b), no such mapping for heart rate (HR) has been proposed. In the current study, we simultaneously deconvolve the effects of RV and HR from resting state fMRI. We demonstrate 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 propose an average HR response function that well characterizes our subject population. We find that the voxel-wise morphology of the deconvolved RV responses is preserved when HR is included in the model, and is adequately modeled by Birn et al.’s previously-described respiration response function. We further demonstrate that modeling out RV and HR can significantly alter functional connectivity maps of the default-mode network.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/bff6f996e9c5b570edb80e5525fc10cb241aba69

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In Cardiology and Cardiovascular Medicine

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Citations in the corpus

The role of physiological noise in resting-state functional connectivity (2012, positive): “FMRI studies have also shown that the variability in heart rate is correlated with MRI signal changes throughout gray matter (Chang et al., 2009; Shmueli et al., 2007).”
Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI (2009, background): “1) yields an optimal linear mapping of RVT to fMRI time series in both cued-inspiration paradigms and task-free resting state (Birn et al., 2008; Chang et al., 2009).”
Methods for cleaning the BOLD fMRI signal (2016, neutral): “While the RVT in Chang et al. (2009) represents the root-mean-square (RMS) average over a sliding window and does not rely on the accuracy of detecting respiration peaks and valleys, the RVT in Birn et al. (2006) accounts more explicitly for variations in breathing rate by normalizing the depth by…”
The Oscillating Brain: Complex and Reliable (2009, background): “Finally, an increasing number of studies have drawn attention to the potential artifactual contributions of cardiac and respiratory-related processes to LFO amplitude measures (Bianciardi et al., 2009; Birn et al., 2006; Chang et al., 2009; van Buuren et al., 2009; Yan et al., 2009).”
Sources and implications of whole-brain fMRI signals in humans (2016, methodology): “, 2008), and RV and RVT*RRF models (Chang et al., 2009).”
Exploring the brain network: a review on resting-state fMRI functional connectivity. (2010, background): “Functional connectivity is defined as the temporal dependency between spatially remote neurophysiological events (Aertsen et al., 1989; Friston et al., 1993).”
Don't fear ‘fear conditioning’: Methodological considerations for the design and analysis of studies on human fear acquisition, extinction, and return of fear (2017, neutral): “Many fMRI scanner environments provide an opportunity to obtain data on respiratory and cardiac functioning, which might cause non-neuronal blood-oxygen-level dependent (BOLD) changes (Birn et al., 2009; Chang et al., 2009; Glover et al., 2000).”
Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. (2010, methodology): “In this respect physiological noise, especially low-frequency components related to respiration, is a particular concern (Birn et al. 2006, 2008a,b; Chang et al. 2009; Shmueli et al. 2007; Van Buuren et al. 2009; Wise et al. 2004).”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “…can arise from cardiac pulsation, shifts in the main magnetic field caused by motion of the body during respiration, and variations in the respiratory volume/ rate and cardiac rate that evoke changes in BOLD contrast (Birn et al., 2008; Chang et al., 2009; Dagli et al., 1999; Shmueli et al., 2007).”
Intrinsic Brain Connectivity in Fibromyalgia is Associated with Chronic Pain Intensity (2010, neutral): “11), as well as regressors representing cardiac and respiratory variability, defined by convolving the heart rate time series and respiratory variations with appropriate cardiac and respiratory transfer functions, respectively, as suggested by Chang et al (33) and Birn et al (34).”
A quantitative comparison of NIRS and fMRI across multiple cognitive tasks (2010, neutral): “In non-task responsive regions, physiological (cardiac and respiratory functions (Chang et al., 2009)) or thermal processes likely contribute a higher proportion of signal variance.”
The Nuisance of Nuisance Regression: Spectral Misspecification in a Common Approach to Resting-State fMRI Preprocessing Reintroduces Noise and Obscures Functional Connectivity (2013, background): “measurable noise sources such as scanner drift and headmotion, aswell as periodic physiological signals (e.g., heartbeat and respiration; Birn et al., 2008; Chang et al., 2009), whereas bandpass filtering suppresses all variability in a range of frequencies that are a priori not of interest.”
Anticorrelations in resting state networks without global signal regression (2011, neutral): “Future work in resting state connectivity should consider noise correction methods without global signal regression, such as the one presented in the present study, possibly in combination other physiological noise correction methods such as the ones described in Chang and Glover (2009).”
EEG correlates of time-varying BOLD functional connectivity (2013, neutral): “…regression, choosing instead to use noise corrections based on concurrent physiological recordings, which have been shown to effectively reduce spatially widespread noise arising from low-frequency respiratory volume and cardiac rate fluctuations (Chang et al., 2009 and Chang and Glover, 2009).”