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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    • "R2007a , Mathworks , Inc . , Natick , MA , USA ) were used to determine the cardiac trigger times from the waveforms . RETROICOR ( Glover et al . , 2000 ) was applied to the imaging data to reduce the effects of cardiac and respiratory cycles . In addition , clean - up techniques based on estimated respiratory ( Birn et al . , 2008 ) and cardiac ( Chang et al . , 2009 ) response functions were employed to regress low - frequency BOLD signal fluctuations due to variations in breathing and heart rates . The resulting functional scans were then preprocessed using SPM 5 ( Wellcome Trust Centre for Neuroimaging , UK , http : / / www . fil . ion . ucl . ac . uk / spm / software / spm5 ) : slice timing corr"
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    ABSTRACT: Multivariate pattern analysis (MVPA) has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the mass-univariate approach. Here, we employed MVPA to classify whole-brain activity patterns occurring in single fMRI scans, in order to retrieve binary answers from experiment participants. Five healthy volunteers performed two types of mental task while in the MRI scanner: counting down numbers and recalling positive autobiographical events. Data from these runs were used to train individual machine learning based classifiers that predicted which mental task was being performed based on the voxel-based brain activity patterns. On a different day, the same volunteers reentered the scanner and listened to six statements (e.g., “the month you were born is an odd number”), and were told to countdown numbers if the statement was true (yes) or recall positive events otherwise (no). The previously trained classifiers were then used to assign labels (yes/no) to the scans collected during the 24-second response periods following each one of the statements. Mean classification accuracies at the single scan level were in the range of 73.6% to 80.8%, significantly above chance for all participants. When applying a majority vote on the scans within each response period, i.e., the most frequent label (yes/no) in the response period becomes the answer to the previous statement, 5.0 to 5.8 sentences, out of 6, were correctly classified in each one of the runs, on average. These results indicate that binary answers can be retrieved from whole-brain activity patterns, suggesting that MVPA provides an alternative way to establish basic communication with unresponsive patients when other techniques are not successful.
    Preview · Article · Dec 2015 · Frontiers in Human Neuroscience
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    • "g . , respiration and heart rate ) is essential for BOLD FC analysis ( Chang and Glover , 2009a ) . Motion can be particularly concerning in pediatric , clinical , and el - derly populations ( Power et al . "

    Full-text · Dataset · Oct 2015
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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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