Figure 3.

Performance enhancement attacks only cause minor changes to connectomes

(A) Data are enhanced to predict IQ measurements in ABCD, HCP, and PNC for 100 iterations of different enhancement patterns (all 100 iterations are shown as points; there is a lot of overlap between iterations). The x axis reflects the mean absolute value of the enhancement pattern added at the edge level (i.e., the absolute mean of the enhancement pattern across all participants for the 20% of edges we altered). At x = 0, there is no enhancement. As a larger enhancement pattern is added, the prediction performance (prediction correlation) increases to r > 0.9, although the edge-wise correlation between original and enhanced connectomes is still r ≈ 0.99. In the second row of (A), enhancement attacks are shown to not affect downstream analyses, which included a sex classification model and participant identification (“fingerprinting”) for HCP.

(B) Identification rates by subnetwork between Rest1 original/enhanced and Rest2 connectomes in HCP.

(C) Several graph metrics, including strength, assortativity, and clustering coefficient, were calculated for the original connectomes and enhanced connectomes, using the largest scale of enhancement presented in (A). The correlation between these metrics for original and enhanced connectomes is presented in (C), with error bars representing the SD of the correlation across participants.