AUTHOR=Qing Zhao , Zhang Xin , Ye Meiping , Wu Sichu , Wang Xin , Nedelska Zuzana , Hort Jakub , Zhu Bin , Zhang Bing 

TITLE=The Impact of Spatial Normalization Strategies on the Temporal Features of the Resting-State Functional MRI: Spatial Normalization Before rs-fMRI Features Calculation May Reduce the Reliability

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 13 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2019.01249

DOI=10.3389/fnins.2019.01249

ISSN=1662-453X

ABSTRACT=Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies frequently applied the spatial normalization on fMRI time series before the calculation of temporal features (here referred as “Prenorm”). We hypothesized that calculating the rs-fMRI features, for example, functional connectivity (FC), regional homogeneity (ReHo) or amplitude of low frequency fluctuation (ALFF) in individual space before the spatial normalization (referred as “Postnorm”) can be an improvement to avoid artifacts and increase the results’ reliability. We utilized two datasets: 1) simulated images where temporal signal to noise ratio (tSNR) is kept a constant and 2) An empirical fMRI dataset with fifty healthy young subjects. For simulated images, the tSNR is constant as generated in individual space, but increased after Prenorm and inter-subject variability of tSNR were induced. In contrast, tSNR kept constant after Postnorm. Consistently, for empirical images, higher tSNR, ReHo and FC (default mode network, seed in precuneus) and lower ALFF were found after Prenorm compared to Postnorm. Coefficient of variability of tSNR and ALFF was higher after Prenorm compared to Postnorm. Moreover, the significant correlation was found between simulated tSNR after Prenorm and empirical tSNR, ALFF and ReHo after Prenorm, indicating algorithmic variation in empirical rs-fMRI features. Furthermore, comparing to Prenorm, ALFF and ReHo showed higher intra-class correlation coefficients between two serial scans after Postnorm. Our results indicated that Prenorm may induce algorithmic inter-subject variability on tSNR and reduce its reliability, which also significantly affect ALFF and ReHo. We suggested using Postnorm instead Prenorm for future rs-fMRI studies using ALFF/ReHo.