Evaluation of Physiological Noise Embedded in Resting-state fMRI

• Main Authors: Chih-Chun Yeh, 葉致均
• Other Authors: 吳昌衛
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/74776138368542386718

碩士 === 國立中央大學 === 生物醫學工程研究所 === 101 === Recently, the investigations of resting-state functional magnetic imaging (RS-fMRI) have attracted attention to the neuroscience field for its wide application on psychological exploration and the clinical pathologies. Beyond its proliferation, the mechanism of RS-fMRI remains unknown and under intensive investigations. Although scientists do not clarify the RS-fMRI signal sources, the blood oxygen level-dependent (BOLD)-based RS-fMRI datasets could be influenced by pre-defined noise types, such as instrumental and physiological noise. Therefore, it is claimed that if the noise of RS-fMRI can be reduced, the significance of the functional connectivity is supposed to increase. To this point, we tested three different noise removal methods to investigate their effectiveness on RS-fMRI analyses. We collected RS-fMRI data from two 3 Tesla (T) scanners, and measured the physiological monitoring unit, including respiration and cardiac pulsation signals, simultaneously. For evaluating the effect of physiological noise, we used three ways to estimate the physiological noise: (1) impulse response function (IRF), (2) RETROICOR and (3) the combination of IRF and RETROICOR (IRFRETRO). Then we compared the effect of these methods on five voxel-based RS-fMRI indices (ALFF, fALFF, ReHo, spatial and temporal SNR), including different brain regions: whole brain (WB), orbitofrontal cortex (OFC), visual cortex (VC) and amygdala (AMY) to further evaluate the regional characteristics. The result showed that the physiological noise has minimal effect on the five RS-fMRI indices, no matter which type of noise removal methods (IRF, RETROICOR or IRFRETRO) was applied. In the regional comparison,, only VC had unique characteristics compared to other regions, especially for ReHo. In the scanner comparison, Skyra possessed higher spatial and temporal SNR than Trio. More specifically, the RS-fMRI signals of WB and VC were dominated by instrumental noise in Trio scanner, whereas the Skyra scanner had higher between-subject variability on spatial SNR. In conclusion, inclusion of the noise removal on RS-fMRI signal may be unnecessary for the five indices. The observations implied that whether we should consider the physiological noise (respiration and cardiac pulsation) as a ‘pure noise’ in RS-fMRI signal remains to be discussed.