A practitioner's guide to geospatial analysis in a neuroimaging context

• DOI: 10.1002/dad2.12413
• Publication Date: 2023-03-16T16:38:43Z
• Authors: Julie K. Wisch; Ganesh M Babulal; Kalen Petersen; Peter R. Millar; Enbal Shacham; Stephen Scroggins; Anna H. Boerwinkle; Shaney Flores; Sarah Keefe; Brian A. Gordon; John C. Morris; Beau M. Ances
• Keywords: Geriatrics; epidemiologic methods; ICTS (Institute of Clinical and Translational Sciences); Medicine and Health Sciences; RC952-954.6; magnetic resonance imaging; brain imaging; Neurology. Diseases of the nervous system; 16. Peace & justice; RC346-429; 333; Research Articles

AbstractIntroductionHealth disparities arise from biological‐environmental interactions. Neuroimaging cohorts are reaching sufficiently large sample sizes such that analyses could evaluate how the environment affects the brain. We present a practical guide for applying geospatial methods to a neuroimaging cohort.MethodsWe estimated brain age gap (BAG) from structural magnetic resonance imaging (MRI) from 239 city‐dwelling participants in St. Louis, Missouri. We compared these participants to population‐level estimates from the American Community Survey (ACS). We used geospatial analysis to identify neighborhoods associated with patterns of altered brain structure. We also evaluated the relationship between Area Deprivation Index (ADI) and BAG.ResultsWe identify areas in St. Louis, Missouri that were significantly associated with higher BAG from a spatially representative cohort. We provide replication code.ConclusionWe observe a relationship between neighborhoods and brain health, which suggests that neighborhood‐based interventions could be appropriate. We encourage other studies to geocode participant information to evaluate biological‐environmental interaction.