Fig. 2.

Average Dice similarity between matched parcellations vs. parcellation level, calculated for parcellations from different datasets of the same subject before (A) and after (B) joining split clusters. Reproducibility results are shown for the data-set one subject which was scanned twice for 60 min. The number of parcels is the total across both hemispheres. Region growing approach with ~1000 (RG₁₀₀₀) and ~3000 (RG₃₀₀₀) seeds outperforms all other tested approaches over a range of parcellation resolutions, especially after parcel joining (B). Also shown is a small subset of the results obtained with other methods (including the next best performing method, NCUTS (NC IC) (Shi et al., 2000) used in Craddock et al. (2011), a spectral clustering approach to optimise network modularity (MC IC, black) (Newman, 2006), a hierarchical clustering approach using Ward's linkage rule (HW1C) (Ward, 1963) and the infomap algorithm (IM IC) (Ward, 1963) as used in Power et al., 2011, all with the same locally restricted correlation similarity measure. For comparison, the results obtained by the same approaches with a sparser similarity matrix (a correlation matrix in which values were thresholded so that only 1% of the entries in each column/row were non-zero (Power et al., 2011)) are also shown ({NC, MC, HW} t_0.01C).).