A practitioner's guide to geospatial analysis in a neuroimaging context. Issue 1 (16th March 2023)

• Record Type: Journal Article
• Authors: Wisch, Julie K.; Babulal, Ganesh M; Petersen, Kalen; Millar, Peter R.; Shacham, Enbal; Scroggins, Stephen; Boerwinkle, Anna H.; Flores, Shaney; Keefe, Sarah; Gordon, Brian A.; Morris, John C.; Ances, Beau M.
• Is Part Of: Alzheimer's & dementia. Volume 15:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-16
• Subjects: brain imaging -- epidemiologic methods -- magnetic resonance imaging; Alzheimer's disease -- Periodicals; Alzheimer's disease -- Diagnosis -- Periodicals; Dementia -- Periodicals; Dementia -- Diagnosis -- Periodicals; 616.831
• DOI: 10.1002/dad2.12413
• Languages: English
• ISSNs: 2352-8729

Abstract

Abstract: Introduction: Health 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. Methods: We 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. Results: We identify areas in St. Louis, Missouri that were significantly associated with higher BAG from a spatially representative cohort. We provide replication code. Conclusion: We 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.