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Global urbanicity is associated with brain and behaviour in young people

Nature Human Behaviour volume 6, pages 279–293 (2022)Cite this article

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Abstract

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.

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Fig. 1: Schematic summary of study design.
Fig. 2: Maps of satellite-based measures of urbanicity in China and Europe.
Fig. 3: Correlations of UrbanSat with brain structure.
Fig. 4: Correlations of UrbanSat with WNFC and BNFC.
Fig. 5: Susceptibility periods of behaviour using DLM and multiple mediation of UrbanSat–brain–behaviour.

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Data availability

All the data are available from the authors upon reasonable request and with permissions of the CHIMGEN and IMAGEN consortia.

Code availability

Custom code that supports the findings of this study is available from the corresponding author upon request.

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Acknowledgements

This work was partly supported by the National Key Research and Development Program of China (grant no. 2018YFC1314301 to C.Y.), National Natural Science Foundation of China (grant no. 82001797 to J.X., 82030053 to C.Y., 81801687 to Q. Li and 81701668 to H.L.), National Key Research and Development Program of China (grant no. 2017YFA0604401 to L.Y. and 2019YFA0606601 to L.Y.), National Key Scientific and Technological Infrastructure project ‘Earth System Science Numerical Simulator Facility’ (EarthLab), European Union-funded FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT-2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (PR-ST-0416-10004), Human Brain Project (HBP SGA 2, 785907 and HBP SGA 3, 945539), Medical Research Council Grant ‘c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), National Institute of Health (NIH) (R01DA049238, A decentralized macro and micro gene-by-environment interaction analysis of substance use behaviour and its brain biomarkers), National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; Forschungsnetz AERIAL 01EE1406A, 01EE1406B), Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-2, SFB 940, TRR 265, NE 1383/14-1), Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1) and National Institutes of Health (NIH) funded ENIGMA (grants 5U54EB020403-05 and 1R56AG058854-01). Further support was provided by grants from the ANR (ANR-12-SAMA-0004, AAPG2019-GeBra), Eranet Neuron (AF12-NEUR0008-01-WM2NA; and ANR-18-NEUR00002-01- ADORe), Fondation de France (00081242), Fondation pour la Recherche Médicale (DPA20140629802), Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, Fondation de l’Avenir (grant AP-RM-17-013), Fédération pour la Recherche sur le Cerveau; National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1), and NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence and The Science&Technology Development Fund of Tianjin Education Commission for Higher Education (grant no. 2019KJ195 to J.X.). Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2049 – 390688087 Falls mehrere Projekte genannt werden müssen, könnte man das z.B. so gestalten: This work was supported by the Deutsche Forschungsgemeinschaft (Collaborative Research Grant SFB 958 (to DS (A5), SJS (A3, A6), CR (A5)) and Excellence Strategy’ – EXC-2049––390688087 (to DS, SJS, and CR). The authors thank C. Li, Department of Health Statistics, College of Public Health, Tianjin Medical University for statistical advice. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, P. R. China

    Jiayuan Xu, Wen Qin, Feng Liu, Lining Guo, Nana Liu, Huaigui Liu & Chunshui Yu

  2. Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology and Neuroscience, SGDP Centre, King’s College London, London, UK

    Jiayuan Xu, Congying Chu & Alex Ing

  3. Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, P. R. China

    Xiaoxuan Liu, Conghong Huang, Peng Gong & Le Yu

  4. College of Information Engineering, Tianjin University of Commerce, Tianjin, P. R. China

    Qiaojun Li

  5. New Light Technologies Inc., Washington, DC, USA

    Ran Goldblatt

  6. Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China

    Qiang Luo

  7. State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenome Institute, Fudan University, Shanghai, China

    Qiang Luo

  8. Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Jingliang Cheng

  9. Department of Radiology, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital, Zhengzhou, China

    Meiyun Wang

  10. Department of Medical Imaging, Second Hospital of Hebei Medical University, Shijiazhuang, China

    Zuojun Geng

  11. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Wenzhen Zhu

  12. Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China

    Bing Zhang

  13. Department of Radiology, Xiangya Hospital, Central South University, Changsha, China

    Weihua Liao

  14. Department of Medical Imaging, First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China

    Shijun Qiu

  15. Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, China

    Hui Zhang

  16. Department of Radiology, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China

    Xiaojun Xu

  17. Department of Radiology, First Affiliated Hospital of Anhui Medical University, Hefei, China

    Yongqiang Yu

  18. Department of Radiology, Affliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China

    Bo Gao

  19. Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China

    Tong Han

  20. Functional and Molecular Imaging Key Lab of Shaanxi Province and Department of Radiology, Tangdu Hospital, Military Medical University of PLA Airforce (Fourth Military Medical University), Xi’an, China

    Guangbin Cui

  21. Department of Radiology, Hainan General Hospital, Haikou, China

    Feng Chen

  22. Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China

    Junfang Xian

  23. Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China

    Jiance Li

  24. Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China

    Jing Zhang

  25. State Key Laboratory of Cognitive Neuroscience and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Xi-Nian Zuo

  26. Department of Radiology, Qilu Hospital of Shandong University, Jinan, China

    Dawei Wang

  27. Department of Radiology, Tianjin First Center Hospital, Tianjin, China

    Wen Shen

  28. Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, China

    Yanwei Miao

  29. Department of Radiology, Pingjin Hospital, Logistics University of Chinese People’s Armed Police Forces, Tianjin, China

    Fei Yuan

  30. Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China

    Su Lui

  31. Department of Radiology, First Affiliated Hospital of USTC, Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science and Technology of China, Hefei, China

    Xiaochu Zhang

  32. Department of Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China

    Kai Xu

  33. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, China

    Longjiang Zhang

  34. Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China

    Zhaoxiang Ye

  35. Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Tobias Banaschewski, Frauke Nees & Sarah Hohmann

  36. Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    Gareth J. Barker

  37. Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland

    Arun L. W. Bokde

  38. Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    Herta Flor & Frauke Nees

  39. Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany

    Herta Flor

  40. NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France

    Antoine Grigis & Dimitri Papadopoulos Orfanos

  41. Department of Psychiatry and Psychology, University of Vermont, Burlington, VT, USA

    Hugh Garavan

  42. Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, Nottingham, UK

    Penny Gowland

  43. Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Berlin, Germany

    Andreas Heinz & Henrik Walter

  44. Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany

    Rüdiger Brühl

  45. Ecole Normale Supérieure Paris-Saclay, Université Paris-Saclay, CNRS, Centre Borelli, INSERM U1299 “Trajectoires Développementales & Psychiatrie”, Gif-sur-Yvette, France

    Jean-Luc Martinot

  46. Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 Neuroimaging and Psychiatry, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; Psychiatry Department 91G16, Orsay Hospital, Orsay, France

    Eric Artiges

  47. Institute National de la Santé et de la Recherche Médicale, UMR 992 INSERM, CEA, Faculté de médecine, Université Paris-Sud, Université Paris-Saclay NeuroSpin, Gif-sur-Yvette, France

    Herve Lemaitre

  48. Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada

    Tomáš Paus

  49. Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, Canada

    Tomáš Paus

  50. Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany

    Luise Poustka

  51. Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Lauren Robinson & Edward D. Barker

  52. Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany

    Juliane H. Fröhner & Michael N. Smolka

  53. School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland

    Robert Whelan

  54. Department of Psychiatry and Psychotherapy CCM, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany

    Jeanne Winterer

  55. Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany

    Jeanne Winterer

  56. Center for Wireless and Population Health Systems, Department of Family and Preventive Medicine and Calit2’s Qualcomm Institute, University of California San Diego, La Jolla, CA, USA

    Kevin Patrick

  57. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA

    Vince Calhoun

  58. Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Department of Pharmacology, Tianjin Medical University, Tianjin, P. R. China

    Mulin Jun Li

  59. School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, P. R. China

    Meng Liang

  60. Department of Geography and Department of Earth Sciences, University of Hong Kong, Hong Kong, China

    Peng Gong

  61. Google, Inc., Mountain View, CA, USA

    Nicholas Clinton

  62. Predictive Clinical Neuroscience Group at the Donders Institute, Radboud University Medical Center, Nijmegen, the Netherlands

    Andre Marquand

  63. CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, P. R. China

    Chunshui Yu

  64. Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology of Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, P. R. China

    Gunter Schumann

  65. Centre for Population Neuroscience and Stratified Medicine (PONS), Charite Mental Health, Dept. of Psychiatry and Psychotherapy, CCM, Charite Universitätsmedizin Berlin, Berlin, Germany

    Gunter Schumann

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  1. Jiayuan Xu
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  4. Ran Goldblatt
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Consortia

the CHIMGEN

  • Jiayuan Xu
  • , Wen Qin
  • , Feng Liu
  • , Lining Guo
  • , Nana Liu
  • , Huaigui Liu
  • , Jingliang Cheng
  • , Meiyun Wang
  • , Zuojun Geng
  • , Wenzhen Zhu
  • , Bing Zhang
  • , Weihua Liao
  • , Shijun Qiu
  • , Hui Zhang
  • , Xiaojun Xu
  • , Yongqiang Yu
  • , Bo Gao
  • , Tong Han
  • , Guangbin Cui
  • , Feng Chen
  • , Junfang Xian
  • , Jiance Li
  • , Jing Zhang
  • , Xi-Nian Zuo
  • , Dawei Wang
  • , Wen Shen
  • , Yanwei Miao
  • , Fei Yuan
  • , Su Lui
  • , Xiaochu Zhang
  • , Kai Xu
  • , Longjiang Zhang
  • , Zhaoxiang Ye
  • , Meng Liang
  •  & Chunshui Yu

IMAGEN Consortia

  • Tobias Banaschewski
  • , Gareth J. Barker
  • , Arun L. W. Bokde
  • , Herta Flor
  • , Antoine Grigis
  • , Hugh Garavan
  • , Penny Gowland
  • , Andreas Heinz
  • , Rüdiger Brühl
  • , Jean-Luc Martinot
  • , Eric Artiges
  • , Frauke Nees
  • , Dimitri Papadopoulos Orfanos
  • , Herve Lemaitre
  • , Tomáš Paus
  • , Luise Poustka
  • , Lauren Robinson
  • , Sarah Hohmann
  • , Juliane H. Fröhner
  • , Michael N. Smolka
  • , Henrik Walter
  • , Robert Whelan
  • , Jeanne Winterer
  • , Edward D. Barker
  •  & Gunter Schumann

Contributions

G.S., C.Y. and J.X. designed the study. J.X., C.Y. and G.S. wrote the manuscript. All authors critically reviewed the manuscript. X.L., Q. Li, R.G., W.Q., F.L., C.C., Q. Luo, A.I., L.G., N.L., H.L., C.H., K.P., V.C., M.J.L., M.L., P.G., E.D.B., N.C., A.M., L.Y., C.Y. and G.S. were the principal investigators. J.C., M.W., Z.G., W.Z., B.Z., W.L., S.Q., H.Z., X.X., Y.Y., B.G., T.H., G.C., F.C., J.X., J.L., J.Z., X.Z., D.W., W.S., Y.M., F.Y., S.L., X.Z., K.X., L.Z., Z.Y., T.B., G.B., A.L.W.B., H.F., A.G., H.G., P.G., A.H., R.B., J.M., E.A., F.N., D.P.O., H. L., T.P., L.P., L.R., S.H., J.H.F., M.N.S., H.W., R.W. and J.W. acquired the data. A.M. supervised the statistical analysis. J.X. and Q. Li analysed the data.

Corresponding authors

Correspondence to Chunshui Yu or Gunter Schumann.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 A flow diagram of sample selection in CHIMGEN (a) and IMAGEN (b).

BDI, Beck depression inventory; BTG, ball tossing games task; BL, IMAGEN baseline assessment acquired at 14 years; CIDI-AS, Anxiety Screening from the Composite International Diagnostic Interview; CVLT-II, the second edition of California verbal learning test; DAWBA-GA, Generalized Anxiety Scale from The Development and Well-Being Assessment Interview; FC, functional connectivity; FU2, IMAGEN second follow up assessment acquired at 19 years; FU2-BL, IMAGEN FU2-BL measures brain changes rate between BL of 14 years and FU2 of 19 years; GNG, go/no-go task; IRI, Interpersonal Reactivity Index; RSQ, Ruminating Scale Questionnaire; SA, state anxiety; SBM, surface-based morphometry; SDMT, symbol digit modalities test; TA, trait anxiety; TBSS, tract-based spatial statistics; VBM, voxel-based morphometry.

Extended Data Fig. 2 Schematic summary of multiple imputation and confirmatory factor analysis.

a. A flow diagram for multiple imputation and confirmatory factor analysis. b, c. Sensitivity analysis results in voxel-wise correlations of ten imputed UrbanSat (b) and combined UrbanSat (c) with brain GMV in CHIMGEN (FWE Pc<0.05). Each imputed UrbanSat from MICE imputation before 18 years show a significant negative correlation with left mPFC volume and a significant positive correlation with cerebellar volume adjusting confounding covariates (FWE Pc<0.05) (b), similar to the results derived from combined UrbanSat score following Rubin’s rule (c). d. The estimated fractions of missing information (FMI) of UrbanSat were low for the GMVs of left-mPFC-ROI (FMI = 1.01%) and cerebellum-ROI (FMI = 1.19%). UrbanSat was still correlated with mPFC-GMV (P < 0.001) and cerebellum-GMV (P < 0.001) after pooling using mice R package in CHIMGEN. e. Non-imputed mean UrbanSat before 18 years still show a significant negative correlation with mPFC-GMV and a significant positive correlation with cerebellar-GMV adjusting confounding covariates (FWE Pc<0.05) (n = 1460). CFA, confirmatory factor analysis; FMI, fractions of missing information; L, left; MICE, multivariate imputation by chained equations; mPFC, medial prefrontal cortex; R, right. S, satellite; Y, years.

Extended Data Fig. 3 Histograms of UrbanSat in each center of CHIMGEN (a) and IMAGEN (b).

TMUGH, Tianjin Medical University General Hospital; TMUCIH, Tianjin Medical University Cancer Institute and Hospital; TFCH, Tianjin First Center Hospital; CPAPFLUPH, Pingjin Hospital, Logistics University of Chinese People’s Armed Police Forces; THH, Tianjin Huanhu Hospital; HMUSH, The Second Hospital of Hebei Medical University; SMUFH, The First Hospital of Shanxi Medical University; DMUFAH, The First Affiliated Hospital of Dalian Medical University; NMUDTH, Drum Tower Hospital, Medical School of Nanjing University; XMUAH, The Affiliated Hospital of Xuzhou Medical University; ZUSAH, The Second Affiliated Hospital of Zhejiang University; WMUFAH, The First Affiliated Hospital of Wenzhou Medical University; WMUSAH, The Second Affiliated Hospital of Wenzhou Medical University; AMUFAH, The First Affiliated Hospital of Anhui Medical University; USTC, University of Science and Technology of China; SUQH, Qilu Hospital of Shandong University; YYH, Yantai Yuhuangding Hospital; ZUPH/HPPH, Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital; ZUFAH, The First Affiliated Hospital of Zhengzhou University; HUSTTH, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology; CSUXH, Xiangya Hospital, Central South University; GUCMFAH, The First Affiliated Hospital of Guangzhou University of Chinese Medicine; HGH, Hainan General Hospital; FMMUTH, Tangdu Hospital, the Military Medical University of PLA Airforce (Fourth Military Medical University); LUSH, Lanzhou University Second Hospital; SUWCH, West China Hospital of Sichuan University; ZUPH, Zhengzhou University People’s Hospital; NMUJH, Jinling Hospital, Medical School of Nanjing University.

Extended Data Fig. 4 Correlations of UrbanSat with brain GMV, SA and CT in CHIMGEN and IMAGEN.

a. Uncorrected correlation statistical maps of UrbanSat with brain GMV in CHIMGEN under non-parametric permutation testing (n = 2176). b. Correlations of UrbanSat with brain GMV in CHIMGEN under Pc<0.05 in TFCE-FWE using non-parametric permutation testing (n = 2176). c, d. Uncorrected correlation statistical maps of UrbanSat with brain GMV in CHIMGEN (c) and IMAGEN-FU2 (d) under parametric testing. e. The overlap results (yellow) in the voxel-wise correlation of mean UrbanSat before 18 years with brain GMV in CHIMGEN (red) and IMAGEN-FU2 (green) after controlling confounders (FWE Pc<0.05). f,g. Uncorrected vertex-wise correlation maps of UrbanSat with surface area (f) and cortical thickness (g) in CHIMGEN (n = 2164). h,i. The mPFC-ROI projected onto the volumetric map (h) and fsaverage surface in Freesurfer (i).

Extended Data Fig. 5 Voxel-wise correlations of individual satellite measures with brain GMV in CHIMGEN (n = 2176) (a-e) and IMAGEN (n = 415) (f-j).

a–e. In CHIMGEN, there are significant negative correlations of mean night-time light (a) and population density (e) with mPFC GMV and positive correlations with cerebellar GMV after controlling confounders (FWE, Pc<0.05); There are significant negative correlations of mean built-up with mPFC GMV (b) and of mean cropland with cerebellar GMV (c); There are no correlations of mean NDVI with brain GMV (d). f–j. In IMAGEN, there are significant negative correlations of mean night-time light (f), mean built-up (g) and population density (j) with mPFC GMV and positive correlations with cerebellar GMV after controlling confounders (FWE, Pc<0.05); There are no correlations of mean cropland (h) and NDVI (i) with brain GMV. L, left; mPFC, medial prefrontal cortex; NDVI, normalized difference vegetation index; R, right.

Extended Data Fig. 6 Forest plot of meta-analysis in CHIMGEN and IMAGEN-FU2.

Effect size of correlations of UrbanSat with mPFC GMV (a), cerebellar GMV (b), mPFC CT (c), mPFC SA (d), WNFCs in aDMN (e), CN (f), mVN (g) and lVN (h), BNFCs of aDMN-CN (i), aDMN-ECN (j), aDMN-rFPN (k) and rFPN-lFPN (l) for meta-analysis in CHIMGEN and IMAGEN-FU2. We exclude SUWCH center from CHIMGEN for all meta-analysis and Dublin center from IMAGEN for the meta-analysis of brain functional features, because there are only 8 and 10 participants from each site, which more than the numbers of covariates while performing Spearman correlation analysis.

Extended Data Fig. 7 Susceptibility analysis of individual satellite measures with brain (a-d) and behaviors (e-h) using distributed lag models in CHIMGEN.

a. There are significant associations of lifetime night-time light with the mPFC-ROI GMV (ages of 4–14 years) and SA (5–12 years), WNFC in aDMN (3–11 years) during childhood and adolescence, with cerebellum-ROI GMV (3–7 years), WNFCs in CN (0–6 years), mVN (0–6 years), lVN (3–10 years), BNFCs in aDMN-CN (4–7 years), aDMN-ECN (4–6 years), aDMN-rFPN (4–6 years) and rFPN-lFPN (4–6 years) during childhood in CHIMGEN. b. There are significant associations of lifetime built-up% with the mPFC-ROI GMV (5–16 years) and WNFC in aDMN (4–14 years) during childhood and adolescence, with WNFCs in mVN and lVN (14–20 years) during adolescence, with mPFC-ROI SA (5–7 years), cerebellum-ROI GMV (1–10 years), WNFC in CN (1–10 years), BNFCs in aDMN-CN (4–10 years), aDMN-ECN (5–7 years), aDMN-rFPN (4–10 years) and rFPN-lFPN (4–6 years) during childhood in CHIMGEN. c. There are significant associations of lifetime cropland% with the mPFC-ROI GMV (5–15 years) during childhood and adolescence, with mPFC-ROI SA (5–6 years), cerebellum-ROI GMV (4–6 years), WNFCs in aDMN (4–6 years), CN (4–6 years) and lVN (4–10 years), BNFCs in aDMN-CN (0–9 years), aDMN-ECN (2–7 years), aDMN-rFPN (4–10 years) and rFPN-lFPN (4–6 years) during childhood in CHIMGEN. d. We find significant associations of lifetime NDVI with the mPFC-ROI GMV (5–15 years) and BNFC in rFPN-lFPN (6–17 years) during childhood and adolescence, with WNFCs in aDMN (5 years old) and CN (5 years old), BNFCs in aDMN-CN (4–11 years) and aDMN-rFPN (4–10 years) during childhood in CHIMGEN. There are significant correlations of lifetime night-time light (e), built-up% (f), cropland % (g) and NDVI (h) with reaction time for perspective-taking performance during adolescence (ages of 5–16 years for night-time light, 4–17 years for built-up %, 5–19 years for cropland % and 4–17 years for NDVI) in CHIMGEN. Significant correlations of lifetime night-time light (e), built-up % (f), cropland % (g) and NDVI (h) with increasing depression measured by BDI are also observed during childhood in CHIMGEN (0–6 years for night-time light, 2–9 years for built-up %, 0–9 years for cropland % and 3–11 years for NDVI). The y-axis represents the changes of brain behaviors associated with an increase of interquartile range of individual satellite measures; the x-axis is individual satellite measure lag in ages. Grey areas indicate 95% CIs. A susceptibility window is identified for the ages where the estimated pointwise 95% CI (shaded area) does not include zero. The blue solid lines indicate negative correlations and red ones indicated positive correlations. aDMN, anterior default mode network; BDI, Beck depression index; BNFC, between-network functional connectivity; CN, cerebellar network; CT, cortical thickness; GMV, grey matter volume; lVN, lateral visual network; mPFC, medial prefrontal cortex; mVN, medial visual network; RTpt, reaction time for perspective-taking; SA, surface area; WNFC, within-network functional connectivity.

Extended Data Fig. 8 Seventeen RSNs identified by independent component analysis in CHIMGEN.

aDMN, anterior default mode network; AN, auditory network; aSN, anterior cingulate cortex part of salience network; CN, cerebellar network; dAN, dorsal attentional network; dSMN, dosal sensorimotor network; ECN, executive control network; inSN, insular part of salience network; lFPN, left frontal parietal network; LN, language network; lVN, lateral visual network; mVN, medial visual network; pDMN, posterior default mode network; PN, precuneus network; rFPN, right frontal parietal network; RSNs, resting-state networks; vAN, ventral attentional network; vSMN, ventral sensorimotor network.

Extended Data Fig. 9 Voxel-wise correlations of individual satellite measures with WNFCs and BNFCs in CHIMGEN (n = 2156) and IMAGEN (n = 315).

a–f. In CHIMGEN, there are negative correlations (blue) of mean UrbanSat (a) and mean night-time light before 18 years (b) with WNFC in the mPFC of the aDMN, positive correlations (red) with WNFCs in the left CV of the CN and left LG of the mVN and lVN (FWE Pc<0.05). c. There are negative correlations (blue) of mean built-up% before 18 years with WNFC in the mPFC of the aDMN and positive correlations (red) with WNFC in the left LG of the lVN (FWE Pc<0.05). d. There are negative correlations (blue) of mean cropland% before 18 years with WNFCs in the CV of the CN and the left LG of the lVN (FWE Pc<0.05). e. There is no correlation of mean NDVI with WNFC of any RSN surviving the multiple correction. f. There are negative correlations (blue) of mean population density from GHSL before 18 years with WNFC in the mPFC of the aDMN, positive correlations (red) with WNFCs in the left CV of the CN and the left LG of the mVN (FWE Pc<0.05). g–l. In IMAGEN, there are negative correlations (blue) of mean UrbanSat (g), night-time light (h), built-up% (i) before 18 years with WNFC in the mPFC of the aDMN, positive correlations (red) with WNFC in the CV of the CN (FWE Pc<0.05). j. There are negative correlations (blue) of mean cropland before 18 years with WNFC in the CV of the CN (FWE Pc<0.05). k. There is no correlation of mean NDVI with WNFC of any RSN surviving the multiple correction. l. There are negative correlations (blue) of mean population density from GHSL before 18 years with WNFC in the mPFC of the aDMN, and positive correlation (red) with WNFC in the CV of the CN (FWE, Pc<0.05). m–p. The mean built-up% (m) (N = 32), cropland% (n) (N = 41), NDVI (o) (N = 1) and population density (p) (N = 52) show correlations with BNFCs in CHIMGEN. The red line indicates positive correlations of UrbanSat with BNFCs and blue line indicated negative correlations. N indicates the numbers of significant correlations of BNFCs. aDMN, anterior default mode network; CV, cerebellar vermis; CN, cerebellar network; GHSL, global human settlement layers; LG, lingual gyrus; lVN, lateral visual network; mPFC, medial prefrontal cortex; mVN, medial visual network; NDVI, normalized difference vegetation index; WNFC, within-network functional connectivity.

Extended Data Fig. 10 The schematic summary of ball tossing game task design, which measures perspective taking and agency performance.

ACT, active agency; 1PP, first-person perspective; 3PP, third-person perspective; PAS, passive agency.

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Supplementary Methods, Results, Tables 1–24 and References.

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Xu, J., Liu, X., Li, Q. et al. Global urbanicity is associated with brain and behaviour in young people. Nat Hum Behav 6, 279–293 (2022). https://doi.org/10.1038/s41562-021-01204-7

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