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

Abstract 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 Study’s 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. Competing Interest Statement The authors have declared no competing interest. Funding Statement The work was supported by the National Institutes of Health (NIH) with grants: R21HD105204, U01MD017867 and T32AA007477. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Email: zikai{at}umich.edu, marymoll{at}umich.edu, sripada{at}umich.edu, jiankang{at}umich.edu The revision has restructured the sections and modified the writing to emphasize the innovation and impacts on population neuroscience research. Data availability The ABCD data used in this paper can be obtained from the NIMH Data Archive (NDA) under Collection 2573 with a data use agreement. The code is available at GitHub: https://github.com/zikaiLin/weighted_image_on_scalar.