Table 1.

Summary of objectives, materials, methods and key novel findings.

Objective	Analytic Method	Data Employed	Key Novel Findings
1 Demonstrate Regional Variation in the Impact of Motion on the BOLD Signal (Figures 1, 2, S1, S2, S3)	Calculate the correlation between BOLD signal and voxel-specific head motion and then perform one-sample t-test.	All datasets	High motion datasets exhibited more pronounced negative motion-BOLD relationships (esp. prefrontal areas). Low motion datasets exhibited more pronounced positive motion-BOLD relationships (esp. primary and supplementary motor areas).
2 Evaluate the Ability of Motion Correction Strategies to Decrease the Impact of Motion on the BOLD Signal at Individual-Level (Figures 3, 4, S4)	After applying the individual-level motion correction strategies on the functional data (after realignment), calculate the correlation between corrected BOLD signal and voxel-specific head motion. Calculate the mean positive correlation and mean negative correlation as summary measures for each participant, and then perform paired t-test to compare strategies.	Cambridge Adults (n = 158; 18 participants removed due to motion)	Only ‘scrubbing’ (FD < 0.2mm) removed negative motion-BOLD relationships. Positive motion-BOLD relationships tended to cluster in primary and supplementary motor areas and remained even after scrubbing, thus may reflect to motion-related neural activity.
3 Evaluate the Ability of Motion Correction Strategies to Decrease Residual Relationships Between Motion and R-fMRI Metrics at Group-Level (Figures 5, 6, S5–S10)	Calculate R-fMRI metrics after motion correction strategies, and then evaluate their correlation with motion across participants. Wilcoxon signed-rank test were used to test the distribution of residual motion effects (absolute correlation) across motion correction strategies.	Cambridge Adults (n = 158)	None of the individual-level motion correction approaches successfully bypass the need for group-level correction for inter-individual differences in R-fMRI related to motion. Z-standardization on subject-level maps reduced relationships between motion and inter-individual differences
4 Examine the Influence of Global Signal Regression (GSR) on the Impact of Motion on the BOLD signal and R-fMRI metrics (Figures 7, S11)	Repeat the analyses in objective 4 and 5, taking in account GSR as well as WM/CSF signal regression.	Cambridge Adults (n = 158)	GSR introduced negative motion-BOLD relationships at individual level. However, GSR was highly effective in removing inter-individual differences related to motion.
5 Examine the Impact of Motion and Motion Correction Strategies on Test-Retest Reliability (Figures 8, S12)	Calculate R-fMRI metrics after motion correction strategies, then evaluate the test-retest reliability via intra-class correlation (ICC). Wilcoxon signed-rank test were employed to compare the reliability across motion correction strategies.	NYU TRT (high motion dataset [n = 11] vs. low motion dataset [n = 11])	Motion appears to reduced test-retest reliability for correlation-based metrics Motion did artifactually increase the reliability of frequency-based metrics (ALFF >> fALFF); correction approaches reduced these increases.
6 Compare Framewise Displacement (FD) Metrics (Figure 9)	Plot the difference between FD_vol and mean_sp FD_vox as a function of mean_sp FD_vox for all time points and all participants. Plot the temporal mean of FD_vol (mean FD_vol) and temporal mean of mean_sp FD_vox (mean [mean_sp FD_vox]).	Cambridge Adults (N = 176)	Overall, there was high concordance among volume-based metrics of FD. Failure to account for rotation can lead to substantial underestimation of FD. The metric by Jenkinson et al. (2002) uniquely accounted for regional variation in motion.