Using predictive validity to compare associations between brain damage and behavior

Lesion-behavior mapping (LBM) provides a statistical map of the association between voxel-wise brain damage and individual differences in behavior. To understand whether two behaviors are mediated by damage to distinct regions, researchers often compare LBM weight outputs by either the Overlap method or the Correlation method. However, these methods lack statistical criteria to determine whether two LBM are distinct vs . the same and are disconnected from a major goal of LBMs: predicting behavior from brain damage. Without such criteria, researchers may draw conclusions from numeric differences between LBMs that are irrelevant to predicting behavior. We developed and validated a Predictive Validity Comparison method (PVC) that establishes a statistical criterion for comparing two LBMs using predictive accuracy: two LBMs are distinct if and only if they provide unique predictive power for the behaviors being assessed. We applied PVC to two lesion-behavior stroke data sets, demonstrating its utility for determining when behaviors arise from the same vs . different lesion patterns. Using region-of-interest based simulations derived from proportion damage from a large data set ( n = 131), PVC accurately detected when behaviors were mediated by different regions (high sensitivity) vs. the same region (high specificity). Both the Overlap method and Correlation method performed poorly on the simulated data. By objectively determining whether two behavioral deficits can be explained by a single vs . distinct patterns of brain damage, PVC provides a critical advance in establishing the brain bases of behavior. We have developed and released a GUI-driven web app to encourage widespread adoption.