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Influence of heart rate on the BOLD signal: The cardiac response function
Open accessAbstract
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.
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Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Time course EPI of human brain function during task activation
Bandettini, P. A., Wong, E. C., Hinks, R. S., Tikofsky, R. S., & Hyde, J. S. (1992) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910250220 · Cited by 1,935
The maturing architecture of the brain's default network
Fair, D. A., Cohen, A. L., Dosenbach, N. U. F., Church, J. A., Miezin, F. M., Barch, D. M., Raichle, M. E., Petersen, S. E., & Schlaggar, B. L. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0800376105 · Cited by 1,314
Competition between functional brain networks mediates behavioral variability
Kelly, A. M. C., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.08.008 · Cited by 1,287
Reduced resting-state brain activity in the “default network” in normal aging
Damoiseaux, J. S., Beckmann, C. F., Arigita, E. J. S., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Rombouts, S. A. R. B. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhm207 · Cited by 1,187 · cited 2×
“, 2006), development (Damoiseaux et al., 2008; Thomason et al., 2008), and dysfunction (Garrity et al.”
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Metadata
- Authors
- Chang, C., Cunningham, J. P., & Glover, G. H.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2009-02
- Volume
- 44
- Issue
- 3
- Pages
- 857-869
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2020333990
- Cited by
- 667
- References
- 16
- FWCI
- 14.81
- Type
- journal article
- Language
- en
- Open access
- green
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