The impact of respiratory and cardiac effects on the phase and magnitude of resting-state fMRI signal

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.

[1]  Catie Chang,et al.  Influence of heart rate on the BOLD signal: The cardiac response function , 2009, NeuroImage.

[2]  Jeff H. Duyn,et al.  An adaptive filter for suppression of cardiac and respiratory noise in MRI time series data , 2006, NeuroImage.

[3]  Ewald Moser,et al.  On the origin of respiratory artifacts in BOLD-EPI of the human brain. , 2002, Magnetic resonance imaging.

[4]  Yingli Lu,et al.  Using voxel-specific hemodynamic response function in EEG-fMRI data analysis , 2006, NeuroImage.

[5]  Yingli Lu,et al.  Using voxel-specific hemodynamic response function in EEG-fMRI data analysis: An estimation and detection model , 2007, NeuroImage.

[6]  Vince D Calhoun,et al.  Magnitude and phase behavior of multiresolution BOLD signal. , 2010, Concepts in magnetic resonance. Part B, Magnetic resonance engineering.

[7]  J C Gore,et al.  A model for susceptibility artefacts from respiration in functional echo-planar magnetic resonance imaging. , 2000, Physics in medicine and biology.

[8]  Peter A. Bandettini,et al.  Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.

[9]  G H Glover,et al.  Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR , 2000, Magnetic resonance in medicine.

[10]  Peter A. Bandettini,et al.  The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration , 2008, NeuroImage.

[11]  A. Anderson,et al.  Respiratory effects in human functional magnetic resonance imaging due to bulk susceptibility changes. , 2001, Physics in medicine and biology.

[12]  X Hu,et al.  Retrospective estimation and correction of physiological fluctuation in functional MRI , 1995, Magnetic resonance in medicine.

[13]  Catie Chang,et al.  Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI , 2009, NeuroImage.