Satellite Imaging of Global Urbanicity relate to Adolescent Brain Development and Behavior

Urbanicity, the impact of living in urban areas, is among the greatest environmental challenges for mental health. While urbanicity might be distinct in different sociocultural conditions and geographic locations, there are likely to exist common features shared in different areas of the globe. Understanding these common and specific relations of urbanicity with human brain and behavior will enable to assess the impact of urbanicity on mental disorders, especially in childhood and adolescence, where prevention and early interventions are likely to be most effective. We constructed from satellite-based remote sensing data a factor for urbanicity that was highly correlated with population density ground data. This factor, ‘UrbanSat’ was utilized in the Chinese CHIMGEN sample (N=831) and the longitudinal European IMAGEN cohort (N=810) to investigate if exposure to urbanicity during childhood and adolescence is associated with differences in brain structure and function in young adults, and if these changes are linked to behavior. Urbanicity was found negatively correlated with medial prefrontal cortex volume and positively correlated with cerebellar vermis volume in young adults from both China and Europe. We found an increased correlation of urbanicity with functional network connectivity within- and between- brain networks in Chinese compared to European participants. Urbanicity was highly correlated with a measure of perceiving a situation from the perspective of others, as well as symptoms of depression in both datasets. These correlations were mediated by the structural and functional brain changes observed. Susceptibility to urbanicity was greatest in two developmental windows during mid-childhood and adolescence. Using innovative technology, we were able to probe the relationship between urban upbringing with brain change and behavior in different sociocultural conditions and geographic locations. Our findings help to identify shared and distinct determinants of adolescent brain development and mental health in different regions of the world, thus contributing to targeted prevention and early-intervention programs for young people in their unique environment. Our approach may be relevant for public health, policy and urban planning globally.

[1]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[2]  W. van den Brink,et al.  Differential relations between juvenile psychopathic traits and resting state network connectivity , 2015, Human brain mapping.

[3]  J. Qiu,et al.  Reduced default mode network functional connectivity in patients with recurrent major depressive disorder , 2019, Proceedings of the National Academy of Sciences.

[4]  A. Thomson,et al.  A global map of urban extent from nightlights , 2015 .

[5]  Tobias J. Hagge,et al.  Physics , 1929, Nature.

[6]  Yonggang Shi,et al.  Brain structure differences between Chinese and Caucasian cohorts: A comprehensive morphometry study , 2018, Human brain mapping.

[7]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

[8]  Bert Brunekreef,et al.  Air Pollution Exposure During Fetal Life, Brain Morphology, and Cognitive Function in School-Age Children , 2018, Biological Psychiatry.

[9]  Jim van Os,et al.  The environment and schizophrenia , 2010, Nature.

[10]  S. Blakemore,et al.  Adolescence as a Sensitive Period of Brain Development , 2015, Trends in Cognitive Sciences.

[11]  A. Arnsten.  Stress signalling pathways that impair prefrontal cortex structure and function , 2009, Nature Reviews Neuroscience.

[12]  M. Rietschel,et al.  The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology , 2010, Molecular Psychiatry.

[13]  M. Knapp,et al.  Mental health, poverty and development , 2012 .

[14]  D. Quattrochi,et al.  Environmental public health applications using remotely sensed data , 2014, Geocarto international.

[15]  K. Lambert,et al.  Brains in the city: Neurobiological effects of urbanization , 2015, Neuroscience & Biobehavioral Reviews.

[16]  Nate Seltenrich.  Remote-Sensing Applications for Environmental Health Research , 2014, Environmental health perspectives.

[17]  Yves Rosseel,et al.  lavaan: An R Package for Structural Equation Modeling , 2012 .

[18]  Fenna M. Krienen,et al.  Segregated Fronto-Cerebellar Circuits Revealed by Intrinsic Functional Connectivity , 2009, Cerebral cortex.

[19]  Wanqing Li,et al.  The default mode network and social understanding of others: what do brain connectivity studies tell us , 2014, Front. Hum. Neurosci..

[20]  Sérgio Freire,et al.  Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer , 2018, Remote. Sens..

[21]  L. Selemon,et al.  Schizophrenia: a tale of two critical periods for prefrontal cortical development , 2015, Translational Psychiatry.

[22]  Shi-Jiang Li,et al.  Intrinsic inter-network brain dysfunction correlates with symptom dimensions in late-life depression. , 2017, Journal of psychiatric research.

[23]  Guillén Fernández,et al.  Stress-induced alterations in large-scale functional networks of the rodent brain , 2015, NeuroImage.

[24]  Haroldo V. Ribeiro,et al.  Rural to Urban Population Density Scaling of Crime and Property Transactions in English and Welsh Parliamentary Constituencies , 2016, PloS one.

[25]  J. Peen,et al.  The current status of urban‐rural differences in psychiatric disorders , 2010, Acta psychiatrica Scandinavica.

[26]  H. V. Ribeiro,et al.  Correction: Rural to Urban Population Density Scaling of Crime and Property Transactions in English and Welsh Parliamentary Constituencies , 2016, PloS one.

[27]  C. Kieling,et al.  Child and adolescent mental health worldwide: evidence for action , 2011, The Lancet.

[28]  S. J. Mayor,et al.  The many faces of population density , 2005, Oecologia.

[29]  Xavier Querol,et al.  Traffic pollution exposure is associated with altered brain connectivity in school children , 2016, NeuroImage.

[30]  Kamran Khodakhah,et al.  Cerebellar modulation of the reward circuitry and social behavior , 2019, Science.

[31]  I. Esau,et al.  Trends in normalized difference vegetation index (NDVI) associated withurban development in northern West Siberia , 2016 .

[32]  Matthew D. Lieberman,et al.  Social, self, (situational), and affective processes in medial prefrontal cortex (MPFC): Causal, multivariate, and reverse inference evidence , 2019, Neuroscience & Biobehavioral Reviews.

[33]  J. Schmahmann,et al.  The cerebellar cognitive affective syndrome. , 1998, Brain : a journal of neurology.

[34]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[35]  Rita R. Colwell,et al.  Using satellite images of environmental changes to predict infectious disease outbreaks. , 2009, Emerging infectious diseases.

[36]  C. Nelson,et al.  Early Adverse Experiences and the Developing Brain , 2016, Neuropsychopharmacology.

[37]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[38]  J. Rose,et al.  Using Satellite Images of Environmental Changes to Predict Infectious Disease Outbreaks , 2009, Emerging Infectious Diseases.

[39]  Jens C. Pruessner,et al.  City living and urban upbringing affect neural social stress processing in humans , 2011, Nature.

[40]  C. Hamani,et al.  High frequency stimulation of the anterior vermis modulates behavioural response to chronic stress: involvement of the prefrontal cortex and dorsal raphe? , 2018, Neurobiology of Disease.

[41]  M. Davidson,et al.  Social and cognitive functioning, urbanicity and risk for schizophrenia , 2007, British Journal of Psychiatry.

[42]  Ago Yeh,et al.  China's post-reform urbanization: retrospect, policies and trends , 2011 .

[43]  M. Montgomery.  The Urban Transformation of the Developing World , 2008, Science.

[44]  Peter B. Jones,et al.  Adult mental health disorders and their age at onset , 2013, British Journal of Psychiatry.

[45]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[46]  Margot J. Taylor,et al.  The developing human brain: age‐related changes in cortical, subcortical, and cerebellar anatomy , 2016, Brain and behavior.

[47]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[48]  Cheuk Y. Tang,et al.  Neuroimaging is a novel tool to understand the impact of environmental chemicals on neurodevelopment , 2014, Current opinion in pediatrics.

[49]  Margot J. Taylor,et al.  The developing human brain: age‐related changes in cortical, subcortical, and cerebellar anatomy , 2016, Brain and behavior.

[50]  Sandro Galea,et al.  Urbanization, urbanicity, and health , 2002, Journal of Urban Health.

[51]  M. Milella,et al.  Population density and developmental stress in the Neolithic: A diachronic study of dental fluctuating asymmetry at Çatalhöyük (Turkey, 7,100-5,950 BC). , 2018, American journal of physical anthropology.

[52]  H T Siegelmann,et al.  The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions , 2015, Scientific Reports.

[53]  Peter Kirsch,et al.  Brain structure correlates of urban upbringing, an environmental risk factor for schizophrenia. , 2015, Schizophrenia bulletin.

[54]  Vikram Patel,et al.  No health without mental health , 2007, The Lancet.

[55]  Christian Gaser,et al.  Associations between urban upbringing and cortical thickness and gyrification. , 2017, Journal of psychiatric research.

[56]  Claus Lamm,et al.  The Neural Substrate of Human Empathy: Effects of Perspective-taking and Cognitive Appraisal , 2007, Journal of Cognitive Neuroscience.