4. Harnessing population diversity: In search for tools of the trade

COEX 

Modern neuroscience is seeing burgeoning population data resources: large-scale datasets with thousands of participant gene expression profiles, brain scanning, and anthropomorphic measures. Such a deep profiling of participants allows us to fully embrace major sources of population diversity – traditionally rarely captured in smaller studies conducted in individual labs. However, big neuroscience datasets are not big small datasets. Emphasis is rebalanced from small, strictly selected, and thus homogenized cohorts towards larger, more representative, and thus diversified cohorts. This shift of context prompts the revision of incumbent modeling practices. In this talk, we will present how predictive tools may fail on new participants due to untracked sources of population diversity. Furthermore, we will present examples of quantitative analytic paradigms and statistical tools that are able to recognize driving factors of population structure, such as ethnicity, height, body composition, gender identity, handedness, language hemisphere dominance, personality, or hormone metabolism. These major sources of population stratification increasingly overshadow the subtle effects that neuroscientists are typically hunting for. That is why dimensions of population stratification need to be treated as effects of interest rather than nuisance variables if the resulting findings are to benefit society as a whole, including marginalized groups. Investing in a new stack of quantitative tools for diversity-aware modeling will bring novel insights into mechanisms behind brain health and disease.