"Urban-Satellite" estimates in the ABCD Study: Linking Neuroimaging and Mental Health to Satellite Imagery Measurements of Macro Environmental Factors.

While numerous studies over the last decade have highlighted the important influence of environmental factors on mental health, globally applicable data on physical surroundings are still limited. Access to such data and the possibility to link them to epidemiological studies is critical to unlocking the relationship of environment, brain and behaviour and promoting positive future mental health outcomes. The Adolescent Brain Cognitive Development (ABCD) Study is the largest ongoing longitudinal and observational study exploring brain development and child health among children from 21 sites across the United States. Here we describe the linking of the ABCD study data with satellite-based "Urban-Satellite" (UrbanSat) variables consisting of 11 satellite-data derived environmental indicators associated with each subject's residential address at their baseline visit, including land cover and land use, nighttime lights, and population characteristics. We present these UrbanSat variables and provide a review of the current literature that links environmental indicators with mental health, as well as key aspects that must be considered when using satellite data for mental health research. We also highlight and discuss significant links of the satellite data variables to the default mode network clustering coefficient and cognition. This comprehensive dataset provides the foundation for large-scale environmental epidemiology research.

Nighttime lights, urban features, household poverty, depression, and obesity.

Nighttime Light Emission (NLE) is associated with diminished mental and physical health. The present study examines how NLE and associated urban features (e.g., air pollution, low green space) impact mental and physical wellbeing. We included 200,393 UK Biobank Cohort participants with complete data. The study was carried out in two steps. In Step1, we assessed the relationship between NLE, deprivation, pollution, green space, household poverty and mental and physical symptoms. In Step2, we examined the role of NLE on environment-symptom networks. We stratified participants into high and low NLE and used gaussian graphical model to identify nodes which bridged urban features and mental and physical health problems. We then compared the global strength of these networks in high vs low NLE. We found that higher NLE associated with higher air pollution, less green space, higher economic and neighborhood deprivation, higher household poverty and higher depressed mood, higher tiredness/lethargy and obesity (Rtraining_mean = 0.2624, P training_mean < .001; Rtest_mean = 0.2619, P test_mean < .001). We also found that the interaction between environmental risk factors and mental, physical problems (overall network connectivity) was higher in the high NLE network than in the low NLE network (t = 0.7896, P < .001). In areas with high NLE, economic deprivation, household poverty and waist circumference acted as bridge factors between the key urban features and mental health symptoms. In conclusion, NLE, urban features, household poverty and mental and physical symptoms are all interrelated. In areas with high NLE, urban features associate with mental and physical health problems at a greater magnitude than in areas with low NLE. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12144-022-02754-3.

Global urbanicity is associated with brain and behaviour in young people.

Urbanicity is a growing environmental challenge for mental health. Here, we investigate correlations of urbanicity with brain structure and function, neuropsychology and mental illness symptoms in young people from China and Europe (total n = 3,867). We developed a remote-sensing satellite measure (UrbanSat) to quantify population density at any point on Earth. UrbanSat estimates of urbanicity were correlated with brain volume, cortical surface area and brain network connectivity in the medial prefrontal cortex and cerebellum. UrbanSat was also associated with perspective-taking and depression symptoms, and this was mediated by neural variables. Urbanicity effects were greatest when urban exposure occurred in childhood for the cerebellum, and from childhood to adolescence for the prefrontal cortex. As UrbanSat can be generalized to different geographies, it may enable assessments of correlations of urbanicity with mental illness and resilience globally.