TE-dependence analysis was performed on input data. An initial mask was generated from the first echo using nilearn's compute_epi_mask function. An adaptive mask was then generated, in which each voxel's value reflects the number of echoes with 'good' data. A monoexponential model was fit to the data at each voxel using log-linear regression in order to estimate T2* and S0 maps. For each voxel, the value from the adaptive mask was used to determine which echoes would be used to estimate T2* and S0. Multi-echo data were then optimally combined using the T2* combination method (Posse et al., 1999). Multi-echo data were then optimally combined using the T2* combination method (Posse et al., 1999). Multi-echo data were then optimally combined using the T2* combination method (Posse et al., 1999). Principal component analysis based on the PCA component estimation with a Moving Average(stationary Gaussian) process (Li et al., 2007) was applied to the optimally combined data for dimensionality reduction. A series of TE-dependence metrics were calculated for each component, including Kappa, Rho, and variance explained. Independent component analysis was then used to decompose the dimensionally reduced dataset. A series of TE-dependence metrics were calculated for each component, including Kappa, Rho, and variance explained. Next, component selection was performed to identify BOLD (TE-dependent), non-BOLD (TE-independent), and uncertain (low-variance) components using the Kundu decision tree (v2.5; Kundu et al., 2013).