Characterization and reduction of cardiac- and respiratory-induced noise as a function of the sampling rate (TR) in fMRI

Abstract

It has recently been shown that both high-frequency and low-frequency cardiac and respiratory noise sources exist throughout the entire brain and can cause significant signal changes in fMRI data. It is also known that the brainstem, basal forebrain and spinal cord area are problematic for fMRI because of the magnitude of cardiac-induced pulsations at these locations. In this study, the physiological noise contributions in the lower brain areas (covering the brainstem and adjacent regions) are investigated and a novel method is presented for computing both low-frequency and high-frequency physiological regressors accurately for each subject. In particular, using a novel optimization algorithm that penalizes curvature (i.e. the second derivative) of the physiological hemodynamic response functions, the cardiac -and respiratory-related response functions are computed. The physiological noise variance is determined for each voxel and the frequency-aliasing property of the high-frequency cardiac waveform as a function of the repetition time (TR) is investigated. It is shown that for the brainstem and other brain areas associated with large pulsations of the cardiac rate, the temporal SNR associated with the low-frequency range of the BOLD response has maxima at subject-specific TRs. At these values, the high-frequency aliased cardiac rate can be eliminated by digital filtering without affecting the BOLD-related signal.

Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: Spatial specificity, test–retest reliability and effect of fMRI sampling rate

“…This discrepancy with earlier work is potentially attributable to the inclusion of PETCO 2 signal in the model and simultaneously estimating the three response functions. Our results also show that CRF is the most consistent response across different sampling rates, similar to reported recently by Cordes et al (2014).…”

“…The shapes of our CRF estimates are largely consistent with previous works (Chang, Cunningham et al, 2009;Cordes et al, 2014;Falahpour et al, 2013), as seen in Fig. 4.…”

“…RVT response is consistent between long-and short-TR data, but it is delayed compared to the response reported in Birn et al (2008a). same extent as can be estimated using highly accelerated fMRI acquisitions (TR = 0.323 seconds). Echoing findings by previous studies (Cordes et al, 2014;Wise et al, 2004), our results show that potential aliasing does not appear to fundamentally impede the estimation of the response functions. However, our results also demonstrate that the elimination of aliasing (through increased sampling rate) improves the estimation of physiological effects in the BOLD signal, as reported recently by Cordes et al (2014).…”

“…Echoing findings by previous studies (Cordes et al, 2014;Wise et al, 2004), our results show that potential aliasing does not appear to fundamentally impede the estimation of the response functions. However, our results also demonstrate that the elimination of aliasing (through increased sampling rate) improves the estimation of physiological effects in the BOLD signal, as reported recently by Cordes et al (2014). Our work also validates the utility of multi-slice accelerated fMRI for high-speed whole-brain imaging.…”

Cervical spinal functional magnetic resonance imaging of the spinal cord injured patient during electrical stimulation

“…Fig. 3 The comparison of average activation voxels (number of voxels) and the percentage of signal change (%) at the cervical vertebra levels C5-C6 in axial plane and sagittal plane between the control group and the OSCI group Eur Spine J However, the influence of motion artifacts caused by cerebrospinal fluid fluctuation, heartbeat, and breathing was not eliminated effectively in this study, which led to occasional false activation in the CSF and muscle [28].…”