Paper
15 March 2011 The impact of respiratory and cardiac effects on the phase and magnitude of resting-state fMRI signal
Zikuan Chen, Vince Calhoun
Author Affiliations +
Abstract
Functional magnetic resonance imaging (fMRI) relies on detecting small changes in signal during brain activities, in presence of various noise, including those caused by respiration and cardiac pulsation. In the resting state, there is no explicit task event except the baseline neuroactivities of awakeness and other unknowns. However, the resting state is accompanied with the cardiac and respiration pulsations, which are the explicit non-neuronal physiological sources of fMRI signals. By recording the respiration and cardiac waveforms in synchrony with the fMRI scanning, we may estimate the physiological modulation artifacts in the fMRI dataset by the temporal correlations between the waveforms and the fMRI signal. In this work, we demonstrate that the respiration and cardiac modulation effects on the magnitude and phase components of the complex fMRI signal, including temporal correlation and time latency. In particular, our results show that: 1) the fMRI phase is slightly more modulated by the physiological modulations than its magnitude counterpart; 2) the fMRI signal (both magnitude and phase) shows 1 to 2s latency to respiration stimulus, and 0 to 1s latency to cardiac stimulus. For physiological artifact removal, we compare the band-stop filtering method with the RETROICOR method and find the former can remove the physiological modulations in a stable and consistent manner in frequency domain (stopping the signature frequencies irrespective of asynchrony.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zikuan Chen and Vince Calhoun "The impact of respiratory and cardiac effects on the phase and magnitude of resting-state fMRI signal", Proc. SPIE 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, 79652A (15 March 2011); https://doi.org/10.1117/12.877321
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Cited by 3 scholarly publications.
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KEYWORDS
Functional magnetic resonance imaging

Modulation

Phase shift keying

Linear filtering

Brain

Convolution

Imaging systems

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