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

By K. Shmueli, P. van Gelderen, J. D. de Zwart, Silvina G. Horovitz, M. Fukunaga, J. Jansma, Jeff H. Duyn, G. H. Glover, Li, T. Q. Ress and 3 more. NeuroImage, 2007

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View Paper: https://www.semanticscholar.org/paper/1588f099f17c6b97d8fa315efc997719d5c19529

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Citations in the corpus

An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data (2012, background): “Seventh, while we focused on the effects of in-scanner head motion on functional connectivity, physiologic signals such as heart and respiration rate also have a substantial in ﬂ uence on rsfc-MRI data (Auer, 2008; Birn et al., 2006; Chang and Glover, 2009; Shmueli et al., 2007).”
Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain (2009, background): “There is an ongoing debate about whether these coherent patterns predominantly originate from physiological signals, like cardiac or respiratory oscillations [Shmueli et al., 2007; Wise et al., 2004] or whether they truly reflect synchronization of intrinsic neuronal activity.”
Resting-state functional connectivity reflects structural connectivity in the default mode network. (2009, background): “Although physiologic noise from several sources can certainly contaminate the connectivity maps (Cordes et al. 2001; Birn et al. 2006; Shmueli et al. 2007) the preponderance of evidence now suggests that resting-state fcMRI is a marker of neural connectivity.”
Exploring the brain network: a review on resting-state fMRI functional connectivity. (2010, methodology): “…signals result from physiological processes, like respiratory and cardiac oscillations (Birn et al., 2006; Birn et al., 2008; Chang et al., 2009; Shmueli et al., 2007; Wise et al., 2004) or whether these correlations originate from co-activation in the underlying spontaneous neuronal activation…”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, background): “Previous studies have shown that numerous physiologic processes (e.g., cardiac and respiratory) can contribute artifactual signals to R-fMRI data (Bianciardi et al., 2009; Birn et al., 2006; Chang et al., 2009; Shmueli et al., 2007; Yan et al., 2009).”
Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks (2008, unknown): “Some recent studies have attempted to address the issue of possible TCN confounds due to either cardiac or respiratory signals by removing fMRI signal correlated with cardiac or respiration [Birn et al., 2006; Shmueli et al., 2007], but no agreement on a ‘‘best approach’’ yet exists.”
Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers (2014, unknown): “Similar approaches are taken in Shmueli et al. (2007) and Birn et al. (2006): these ﬁ lter the aspects of the imaging data that demonstrate strong correspondence with the measurements (e.g., in terms of phase or correlation).”
Effects of model-based physiological noise correction on default mode network anti-correlations and correlations (2009, unknown): “Respiration and cardiac processes are known to modulate the BOLD signal (Biswal et al., 1993; Dagli et al., 1999; Jezzard et al., 1993; Shmueli et al., 2007; Weisskoff et al., 1993; Wise et al., 2004), and their effects can be reduced by monitoring respiratory and cardiac cycles throughout the scan…”
Predicting human resting-state functional connectivity from structural connectivity (2009, background): “Alternatively, power in low frequencies may also arise from physiological sources [2].”