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

Julie K. Wisch; Ganesh M. Babulal; Kalen Petersen; Peter R. Millar; Enbal Shacham; Stephen Scroggins; Anna H. Boerwinkle; Shaney Flores; Sarah Keefe; Brian Gordon; John Morris; Beau M. Ances

doi:10.1002/dad2.12413

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

Julie K. Wisch, Ganesh M. Babulal, Kalen Petersen, Peter R. Millar, Enbal Shacham, Stephen Scroggins, Anna H. Boerwinkle, Shaney Flores, Sarah Keefe, Brian Gordon, John Morris, Beau M. Ances

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

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.

Original language	English
Article number	e12413
Journal	Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume	15
Issue number	1
DOIs	https://doi.org/10.1002/dad2.12413
State	Published - Jan 1 2023

Keywords

brain imaging
epidemiologic methods
magnetic resonance imaging

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10.1002/dad2.12413

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Wisch, J. K., Babulal, G. M., Petersen, K., Millar, P. R., Shacham, E., Scroggins, S., Boerwinkle, A. H., Flores, S., Keefe, S., Gordon, B., Morris, J., & Ances, B. M. (2023). A practitioner's guide to geospatial analysis in a neuroimaging context. Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, 15(1), Article e12413. https://doi.org/10.1002/dad2.12413

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title = "A practitioner's guide to geospatial analysis in a neuroimaging context",

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.",

keywords = "brain imaging, epidemiologic methods, magnetic resonance imaging",

author = "Wisch, {Julie K.} and Babulal, {Ganesh M.} and Kalen Petersen and Millar, {Peter R.} and Enbal Shacham and Stephen Scroggins and Boerwinkle, {Anna H.} and Shaney Flores and Sarah Keefe and Brian Gordon and John Morris and Ances, {Beau M.}",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.",

year = "2023",

month = jan,

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doi = "10.1002/dad2.12413",

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AU - Wisch, Julie K.

AU - Babulal, Ganesh M.

AU - Petersen, Kalen

AU - Millar, Peter R.

AU - Shacham, Enbal

AU - Scroggins, Stephen

AU - Boerwinkle, Anna H.

AU - Flores, Shaney

AU - Keefe, Sarah

AU - Gordon, Brian

AU - Morris, John

AU - Ances, Beau M.

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N2 - 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.

AB - 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.

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

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