Figure 3 .

Flowcharts of comparisons with other algorithms. (A) Comparing out-of-sample resting-state homogeneity across different parcellation approaches applied to a single rs-fMRI session. (B) Comparing out-of-sample resting-state homogeneity across different parcellation approaches applied to different lengths of rs-fMRI data. (C) Comparing task inhomogeneity across different approaches. (D) Comparing RSFC-based behavioral prediction accuracies across different approaches. Across all analyses, MS-HBM parcellations were estimated using the trained models from Figure 2A. We remind the reader that the trained MS-HBMs were estimated using the HCP training and validation sets (Fig. 2A), which did not overlap with the HCP test set utilized in the current set of analyses. In the case of analyses (A) and (B), only a portion of rs-fMRI data was used to estimate the parcellations. The remaining rs-fMRI data were used to compute out-of-sample resting-state homogeneity. For analyses (C) and (D), all available rs-fMRI data were used to estimate the parcellations. Finally, we note that the local gradient approach (Laumann2015) does not yield a fixed number of parcels. Thus, the number of parcels is variable within an individual with different lengths of rs-fMRI data, so Laumann2015 was not considered for analysis B. Similarly, the number of parcels is different across participants, so the sizes of the RSFC matrices are different across participants. Therefore, Laumann2015 was also not utilized for analysis D.