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 biologicalenvironmental interaction.

A fresh look at the multi‐level social determinants of disparities in Alzheimer's disease and related dementias

“…In true reflection of the deeply multifaceted nature of this line of inquiry, the topics covered in this special issue span a diverse array of content areas such as SDoH-linked cognitive decline across racial groups, the lack of representativeness in ADRD research, the effects of early life experiences on late-life cognition, the impact of COVID-19 on cognitive impairment in older adults, the economic burden associated with mild cognitive impairment and mild dementia, proposals emanating from workshops and perspectives on improving access to timely diagnosis, differences in performance across racial groups on commonly used instruments for tracking cognitive change, and novel methodologies that can be used to better understand how the environment affects brain health. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] Taken together, the studies in this special issue bring us closer to identifying key constructs related to economic stability, social and community context, access to and quality of health care, and other things that are linked to cognitive outcomes and the progression to ADRD in minoritized older adults. Email: ozioma@medicine.wisc.edu…”

Deriving life‐course residential histories in brain bank cohorts: A feasibility study

“…Residence in high ADI neighborhoods, indicating greater disadvantage, has been associated with numerous adverse health states, [8][9][10][11][12] poorer cognition, 13 cognitive decline and impairment, 14,15 dementia, [16][17][18] postmortem AD plaque and tangle neuropathology, 3,19 cerebral/hippocampal volume, 20 and higher estimated brain age compared to actual age. 21 The mechanism for these associations is not clear 22 but may be to related pathways that include stress, 23 inflammation, 24,25 poorer cognitive reserve, [26][27][28][29] epigenetic modifications, 30 toxic stress of structural inequities and discrimination, [31][32][33][34] and health behaviors and cardiovascular health. [35][36][37] Much of the exposome work to date is cross-sectional in naturemeasuring exposures, including the ADI, at a single location and single time point.…”

“…One validated metric used to assess the social exposome is the area deprivation index (ADI), 7 a measure encompassing income, education, employment, and housing quality by leveraging geo‐discrete US Census and American Community Survey data. Residence in high ADI neighborhoods, indicating greater disadvantage, has been associated with numerous adverse health states, 8–12 poorer cognition, 13 cognitive decline and impairment, 14,15 dementia, 16–18 postmortem AD plaque and tangle neuropathology, 3,19 cerebral/hippocampal volume, 20 and higher estimated brain age compared to actual age 21 . The mechanism for these associations is not clear 22 but may be to related pathways that include stress, 23 inflammation, 24,25 poorer cognitive reserve, 26–29 epigenetic modifications, 30 toxic stress of structural inequities and discrimination, 31–34 and health behaviors and cardiovascular health 35–37 …”

Advanced structural brain aging in preclinical autosomal dominant Alzheimer disease

“…( D ) Brain Age Gap (BAG) for each DIAN participant was calculated as the difference between DBN-predicted and true chronological age. Figure created in part with BioRender.com and modified from [ 33 ] …”