Empirical Evaluations of Slice-Timing, Smoothing, and Normalization Effects in Seed-Based, Resting-State Functional Magnetic Resonance Imaging Analyses

Recently, functional connectivity analyses using spontaneous functional magnetic resonance imaging (fMRI) fluctuations have been applied in the context of neurological and psychiatry diseases. In the analyses procedure, preprocessing steps are commonly utilized in exploring functional connectivity, the same strategy as what was conducted in the fMRI process. However, the effectiveness of these preprocessing steps on resting-state fMRI (rs-fMRI) was rarely investigated, and the significance of preprocessing steps on rs-fMRI needs to be studied. Therefore, the main purpose of the current study was to evaluate the effects of multiple preprocessing procedures, including slice-timing correction, smoothing, and spatial normalization, on rs-fMRI signal. Through a seed-based correlation analysis on the motor network, we empirically estimated three indices of spontaneous fMRI fluctuations induced: correlation coefficients (CC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF (fALFF), with different strategies of three preprocessing steps. Multiple repetition times (TRs=2, 3, 4 sec) were also compared to address the issue of temporal mismatch. In the temporal preprocess, we found that the use of slice-timing correction and different TRs had minimal effects on CC and fALFF. However, ALFF was significantly affected using different TR but not affected by slice-timing correction as well. In the spatial preprocess, fALFF was insensitive to both smoothing and normalization. Smoothing consistently increased spatial extents and C.C., but suppressed ALFF values. Performing normalization before index calculations provided better spatial sensitivity with larger variability in ALFF, whereas performing normalization after index calculations might preserve the ALFF level as in the unnormalized data. Conclusively, the effects of choosing preprocessing parameters and strategies were presented in the current study, providing practical considerations when conducting rs-fMRI analyses.