Why weight? Analytic approaches for large-scale population neuroscience data
| dc.contributor.author | Gard, Arianna M. | |
| dc.contributor.author | Hyde, Luke W. | |
| dc.contributor.author | Heeringa, Steven G. | |
| dc.contributor.author | West, Brady T. | |
| dc.contributor.author | Mitchell, Colter | |
| dc.date.accessioned | 2023-09-11T17:23:24Z | |
| dc.date.available | 2023-09-11T17:23:24Z | |
| dc.date.issued | 2023-01-06 | |
| dc.description | Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund. | |
| dc.description.abstract | Population-based neuroimaging studies that feature complex sampling designs enable researchers to generalize their results more widely. However, several theoretical and analytical questions pose challenges to researchers interested in these data. The following is a resource for researchers interested in using population-based neuroimaging data. We provide an overview of sampling designs and describe the differences between traditional model-based analyses and survey-oriented design-based analyses. To elucidate key concepts, we leverage data from the Adolescent Brain Cognitive Development℠ Study (ABCD Study®), a population-based sample of 11,878 9–10-year-olds in the United States. Analyses revealed modest sociodemographic discrepancies between the target population of 9–10-year-olds in the U.S. and both the recruited ABCD sample and the analytic sample with usable structural and functional imaging data. In evaluating the associations between socioeconomic resources (i.e., constructs that are tightly linked to recruitment biases) and several metrics of brain development, we show that model-based approaches over-estimated the associations of household income and under-estimated the associations of caregiver education with total cortical volume and surface area. Comparable results were found in models predicting neural function during two fMRI task paradigms. We conclude with recommendations for ABCD Study® users and users of population-based neuroimaging cohorts more broadly. | |
| dc.description.uri | https://doi.org/10.1016/j.dcn.2023.101196 | |
| dc.identifier | https://doi.org/10.13016/dspace/r8cu-avn6 | |
| dc.identifier.citation | Gard, A.M., Hyde, L.W. et al. Why weight? Analytic approaches for large-scale population neuroscience data, Elsevier, 2023. | |
| dc.identifier.uri | http://hdl.handle.net/1903/30452 | |
| dc.language.iso | en_US | |
| dc.publisher | Elsevier | |
| dc.relation.isAvailableAt | College of Behavioral & Social Sciences | en_us |
| dc.relation.isAvailableAt | Psychology | en_us |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_us |
| dc.relation.isAvailableAt | University of Maryland (College Park, MD) | en_us |
| dc.subject | population neuroscience | |
| dc.subject | generalizability | |
| dc.subject | convenience sampling | |
| dc.subject | probability sampling | |
| dc.subject | ABCD Study® | |
| dc.title | Why weight? Analytic approaches for large-scale population neuroscience data | |
| dc.type | Article | |
| local.equitableAccessSubmission | No |