Fig. S1: Results of application of bundling with the same parameters used for the group data in the main
manuscript (cthr = 0.7, σ = 5 mm) to six randomly selected individual whole-brain functional connectivity
datasets. The binarization threshold is set to an identical numerical value (z > 0.427), which means the
number of edges in the binary graphs varies due to inter-individual differences. Nevertheless, the
structure in the bundlings is similar.
Fig. S2: Results of application of bundling with the same parameters used for the group data in the main
manuscript (cthr = 0.7, σ = 5 mm) to 20 randomly selected individual datasets from the whole-brain
functional connectivity group data, bundled with the binary threshold set to an identical percentage of
7.5 % of connections remaining. The structure of the bundlings is very similar, although there is variation
in the distribution of the connections due to inter-individual differences.
Fig. S3: Results of application of bundling with the same parameters used for the group data in the main
manuscript (cthr = 0.7, σ = 5 mm) to 20 subgroups with the average connectivity of 20 randomly selected
individual datasets from the group data each. The binarization threshold is set to an identical absolute
value (z = 0.427), which means the number of edges varies.
Fig. S4: Results of application of bundling with the same parameters used for the group data in the main
manuscript (cthr = 0.7, σ = 5 mm) to 20 subgroups with the average connectivity of 20 randomly selected
individual datasets from the group data each. The binarization threshold is set to an identical percentage
of 7.5 % of connections remaining.
Fig. S5: Influence of compatibility threshold and kernel width on the bundling results for the group
functional connectivity data described in the main manuscript, using a binarization threshold of 7.5 %.
The solution we used (cthr = 0.7, σ = 5 mm) is marked with a black rectangle.
Fig. S6: Comparison between functional (blue) and anatomical (red) connectivity. The anatomical
connectivity data is a binary graph derived from DWI data downloaded from the FSL Course website
(fsl.fmrib.ox.ac.uk/fslcourse), and preprocessed with a Nipype workflow
(nipy.sourceforge.net/nipype/users/examples/dmri_connectivity_advanced.html) which uses MRTrix
(brain.org.au/software/mrtrix) for probabilistic fiber tracking.
We bundled both datasets with the same parameters (cthr = 0.7, σ = 5 mm) we used in the main
manuscript. Both bundlings do not necessarily follow anatomical fiber tracts, but are abstract
visualizations of connectivity in anatomical space.