Abstract
The traditional brain mapping approach has greatly advanced our understanding of the localized effect of the brain on behavior. However, the statistically significant brain regions identified by the standard mass univariate models only explain minimal variance in behavior despite increased sample sizes and statistical power, highlighting the nonsparseness of the explanatory signal in the brain. We introduced the Bayesian polyvertex score (PVS-B), a whole-brain prediction framework that aggregates the effect sizes across all vertices to predict individual variability in behavior. The PVS-B estimates the posterior mean effect size at each vertex with the summary statistics from the brain mapping approach and the correlation structure of the imaging phenotype. Empirical data showed that the PVS-B was able to double the variance explained of the total composite cognition score by an nBack fMRI contrast when compared to prediction models based on the mass univariate parameter estimates as well as models based on p-value thresholds. A fivefold improvement in variance explained by the PVS-B was observed using the stop signal fMRI contrast to predict individual variability in the stop signal reaction time. We believe that the PVS-B can shed light on the multivariate investigation of brain-behavioral associations and will empower small scale neuroimaging studies with more reliable and accurate effect size estimates.