In reviewing this impressive series of articles, I was struck by 2 points in particular: (a) the fact that the empirically oriented articles focused on analyses of data from very large samples, with the articles by Friedman, Kern, Hampson, and Duckworth (2014) and Kern, Hampson, Goldbert, and Friedman (2014) highlighting an approach to merging existing data sets through use of "metric bridges" to address key questions not addressable through 1 data set alone, and (b) the fact that the articles as a whole included limited mention of neuroscientific (i.e., brain research) concepts, methods, and findings. One likely reason for the lack of reference to brain-oriented work is the persisting gap between smaller sample size lab-experimental and larger sample size multivariate-correlational approaches to psychological research. As a strategy for addressing this gap and bringing a distinct neuroscientific component to the National Institute on Aging's conscientiousness and health initiative, I suggest that the metric bridging approach highlighted by Friedman and colleagues could be used to connect existing large-scale data sets containing both neurophysiological variables and measures of individual difference constructs to other data sets containing richer arrays of nonphysiological variables--including data from longitudinal or twin studies focusing on personality and health-related outcomes (e.g., Terman Life Cycle study and Hawaii longitudinal studies, as described in the article by Kern et al., 2014).