Population-weighted Image-on-scalar Regression Analyses of Large Scale Neuroimaging Data

Recent advances in neuroimaging modeling highlight the importance of accounting for subgroup heterogeneity in population-based neuroscience research through various investigations in large scale neuroimaging data collection. To integrate survey methodology with neuroscience research, we present an imaging data analysis aiming to achieve population generalizability with screened subsets of data. The Adolescent Brain Cognitive Development (ABCD) Study has enrolled a large cohort of participants to reflect the individual variation of the U.S. population in adolescent development. To ensure population representation, the ABCD Study has released the base weights. We estimated the associations between brain activities and cognitive performance using the functional Magnetic Resonance Imaging (fMRI) data from the ABCD Studys n-back working memory task. Notably, the imaging subsample exhibits differences from the baseline cohort in key child characteristics, and such discrepancies cannot be addressed simply by applying the ABCD base weights. We developed new population weights specific to the subsample and included the adjusted weights in the image-on-scalar regression model. We validated the approach through synthetic simulations and applications to fMRI data from the ABCD Study. Our findings indicate that population weighting adjustments influence association estimates between brain activities and cognition, emphasizing the importance of evaluating validity and generalizability in population neuroscience research.