Figure 4.

Performance enhancement attacks in the SLIM dataset

This example is shown for prediction of state anxiety in the SLIM dataset with resting-state connectomes and rCPM. In the top row, prediction with the original dataset shows poor performance (r ≈ 0). In the second row, as in Figure 2, an enhancement pattern proportional to the state anxiety measure can be added to random edges to enhance performance while maintaining very high correlations between the original and enhanced connectomes (r ≈ 0.99). In the bottom row, an enhancement pattern can be added to specific subnetworks to alter interpretation. Here, we targeted the enhancement pattern to the salience subnetwork, and the resulting coefficients reflect that edges in the salience network dominate the prediction outcome.