but instead of focusing on either the right or left, we formed a composite matrix by combining the streamline counts between homotopic pairs of regions. We, again, enforced a mean density of ρ ≈ 10% by selecting a streamline threshold of 30 streamlines. 

## 5.3. Alternative CHUV datasets - See Figures S3-S6 

We investigated four variants of the CHUV dataset. In the main text we analyzed binary connectivity matrices (average density of ρ ≈ 10%) by applying a threshold to streamline counts. The first two variants were constructed in the same manner but with the threshold level chosen to maintain average densities of ρ ≈ 5% and ρ ≈ 15%. The third variant retained a threshold of ρ ≈ 10% but instead of thresholding streamline counts we thresholded ”fiber density” matrices. The fiber density between nodes u and v is common choice for edge weights in weighted anatomical brain networks, and is defined as the number of streamlines divided by the sum of u and v ’s surface areas (Hagmann et al., 2008; Betzel et al., 2013; Go˜ ni et al., 2014). The fourth variant was constructed by thresholding streamline counts to ρ ≈ 10% but included both left and right cerebral hemispheres. 

## 5.4. Group-average matrices - See Figures S7-S9 

In addition to single-participant modeling, we analyzed group-average con- nectivity matrices for all three datasets (CHUV, HCP, and NKI). Group-average matrices boost the ratio of signal to noise by emphasizing connections that are consistently expressed across subjects, thereby rendering the human connec- tome more clearly. The de facto method for generating group-average matrices is to retain the supra-threshold elements of the [ n × n ] consistency matrix, C , whose element c$_{uv}$ indicates the fraction of all participants in which a connec- tion was present between nodes u and v . The resulting matrix, however, over- expresses short-range connections, as short-range connections are more easily 

