Studying the Spatial Distribution of Physiological Effects on BOLD Signals Using Ultrafast fMRI
MetadataShow full item record
CitationTong, Yunjie, and Blaise deB. Frederick. 2014. “Studying the Spatial Distribution of Physiological Effects on BOLD Signals Using Ultrafast fMRI.” Frontiers in Human Neuroscience 8 (1): 196. doi:10.3389/fnhum.2014.00196. http://dx.doi.org/10.3389/fnhum.2014.00196.
AbstractThe blood-oxygen-level dependent (BOLD) signal in functional MRI (fMRI) reflects both neuronal activations and global physiological fluctuations. These physiological fluctuations can be attributed to physiological low frequency oscillations (pLFOs), respiration, and cardiac pulsation. With typical TR values, i.e., 2 s or longer, the high frequency physiological signals (i.e., from respiration and cardiac pulsation) are aliased into the low frequency band, making it hard to study the individual effect of these physiological processes on BOLD. Recently developed multiband EPI sequences, which offer full brain coverage with extremely short TR values (400 ms or less) allow these physiological signals to be spectrally separated. In this study, we applied multiband resting state scans on nine healthy participants with TR = 0.4 s. The spatial distribution of each physiological process on BOLD fMRI was explored using their spectral features and independent component analysis (ICA). We found that the spatial distributions of different physiological processes are distinct. First, cardiac pulsation affects mostly the base of the brain, where high density of arteries exists. Second, respiration affects prefrontal and occipital areas, suggesting the motion associated with breathing might contribute to the noise. Finally, and most importantly, we found that the effects of pLFOs dominated many prominent ICA components, which suggests that, contrary to the popular belief that aliased cardiac and respiration signals are the main physiological noise source in BOLD fMRI, pLFOs may be the most influential physiological signals. Understanding and measuring these pLFOs are important for denoising and accurately modeling BOLD signals.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12153051
- HMS Scholarly Articles