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Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior

Molecular Psychiatry volume 28, pages 17–27 (2023)Cite this article

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Abstract

Individual differences in human brain structure, function, and behavior can be attributed to genetic variations, environmental exposures, and their interactions. Although genome-wide association studies have identified many genetic variants associated with brain imaging phenotypes, environmental exposures associated with these phenotypes remain largely unknown. Here, we propose that environmental neuroscience should pay more attention on exploring the associations between lifetime environmental exposures (exposome) and brain imaging phenotypes and identifying both cumulative environmental effects and their vulnerable age windows during the life course. Exposome-neuroimaging association studies face several challenges including the accurate measurement of the totality of environmental exposures varied in space and time, the highly correlated structure of the exposome, and the lack of standardized approaches for exposome-wide association studies. By agnostically scanning the effects of environmental exposures on brain imaging phenotypes and their interactions with genomic variations, exposome-neuroimaging association analyses will improve our understanding of causal factors associated with individual differences in brain structure and function as well as their relations with cognitive abilities and neuropsychiatric disorders.

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Fig. 1: Relationships between gene, environment, brain, and behavior, and an example of exposome-neuroimaging associations.
Fig. 2: Examples for data acquisition and analysis in exposome-neuroimaging association studies.

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All the data used in this paper are available upon reasonable request.

References

  1. Deco G, Kringelbach ML. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron. 2014;84:892–905.

    CAS  Google Scholar 

  2. Smith SM, Nichols TE, Vidaurre D, Winkler AM, Behrens TE, Glasser MF, et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci. 2015;18:1565–7.

    CAS  Google Scholar 

  3. Medland SE, Jahanshad N, Neale BM, Thompson PM. Whole-genome analyses of whole-brain data: working within an expanded search space. Nat Neurosci. 2014;17:791–800.

    CAS  Google Scholar 

  4. Mufford MS, Stein DJ, Dalvie S, Groenewold NA, Thompson PM, Jahanshad N. Neuroimaging genomics in psychiatry-a translational approach. Genome Med. 2017;9:102.

    Google Scholar 

  5. Stein JL, Medland SE, Vasquez AA, Hibar DP, Senstad RE, Winkler AM, et al. Identification of common variants associated with human hippocampal and intracranial volumes. Nat Genet. 2012;44:552–61.

    CAS  Google Scholar 

  6. Hibar DP, Stein JL, Renteria ME, Arias-Vasquez A, Desrivieres S, Jahanshad N, et al. Common genetic variants influence human subcortical brain structures. Nature. 2015;520:224–9.

    CAS  Google Scholar 

  7. Grasby KL, Jahanshad N, Painter JN, Colodro-Conde L, Bralten J, Hibar DP, et al. The genetic architecture of the human cerebral cortex. Science. 2020;367:eaay6690.

    CAS  Google Scholar 

  8. Elliott LT, Sharp K, Alfaro-Almagro F, Shi S, Miller KL, Douaud G, et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature. 2018;562:210–6.

    CAS  Google Scholar 

  9. Zhao B, Li T, Yang Y, Wang X, Luo T, Shan Y, et al. Common genetic variation influencing human white matter microstructure. Science. 2021;372:eabf3736.

    CAS  Google Scholar 

  10. Rappaport SM, Smith MT. Epidemiology. Environment and disease risks. Science. 2010;330:460–1.

    CAS  Google Scholar 

  11. Vermeulen R, Schymanski EL, Barabasi AL, Miller GW. The exposome and health: where chemistry meets biology. Science. 2020;367:392–6.

    CAS  Google Scholar 

  12. Sharon G, Sampson TR, Geschwind DH, Mazmanian SK. The central nervous system and the gut microbiome. Cell. 2016;167:915–32.

    CAS  Google Scholar 

  13. Kempermann G. Environmental enrichment, new neurons and the neurobiology of individuality. Nat Rev Neurosci. 2019;20:235–45.

    CAS  Google Scholar 

  14. Wild CP. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomark Prev. 2005;14:1847–50.

    CAS  Google Scholar 

  15. Wild CP. The exposome: from concept to utility. Int J Epidemiol. 2012;41:24–32.

    Google Scholar 

  16. Cui Y, Balshaw DM, Kwok RK, Thompson CL, Collman GW, Birnbaum LS. The exposome: embracing the complexity for discovery in environmental health. Environ Health Perspect. 2016;124:A137–40.

    Google Scholar 

  17. Vrijheid M. The exposome: a new paradigm to study the impact of environment on health. Thorax. 2014;69:876–8.

    Google Scholar 

  18. Santos S, Maitre L, Warembourg C, Agier L, Richiardi L, Basagana X, et al. Applying the exposome concept in birth cohort research: a review of statistical approaches. Eur J Epidemiol. 2020;35:193–204.

    Google Scholar 

  19. Perrin RJ, Fagan AM, Holtzman DM. Multimodal techniques for diagnosis and prognosis of Alzheimer’s disease. Nature. 2009;461:916–22.

  20. Teipel S, Drzezga A, Grothe MJ, Barthel H, Chetelat G, Schuff N, et al. Multimodal imaging in Alzheimer’s disease: validity and usefulness for early detection. Lancet Neurol. 2015;14:1037–53.

    Google Scholar 

  21. Grefkes C, Fink GR. Connectivity-based approaches in stroke and recovery of function. Lancet Neurol. 2014;13:206–16.

    Google Scholar 

  22. Jiang L, Liu J, Wang C, Guo J, Cheng J, Han T, et al. Structural alterations in chronic capsular versus pontine stroke. Radiology. 2017;285:214–22.

    Google Scholar 

  23. Kelly S, Jahanshad N, Zalesky A, Kochunov P, Agartz I, Alloza C, et al. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group. Mol Psychiatry. 2018;23:1261–9.

    CAS  Google Scholar 

  24. Li A, Zalesky A, Yue W, Howes O, Yan H, Liu Y, et al. A neuroimaging biomarker for striatal dysfunction in schizophrenia. Nat Med. 2020;26:558–65.

    CAS  Google Scholar 

  25. Schmaal L, Hibar DP, Samann PG, Hall GB, Baune BT, Jahanshad N, et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol Psychiatry. 2017;22:900–9.

    CAS  Google Scholar 

  26. Gray JP, Muller VI, Eickhoff SB, Fox PT. Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies. Am J Psychiatry. 2020;177:422–34.

    Google Scholar 

  27. van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Busatto GF, et al. Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: results from the ENIGMA ASD Working Group. Am J Psychiatry. 2018;175:359–69.

    Google Scholar 

  28. Cerliani L, Mennes M, Thomas RM, Di Martino A, Thioux M, Keysers C. Increased functional connectivity between subcortical and cortical resting-state networks in autism spectrum disorder. JAMA Psychiatry. 2015;72:767–77.

    Google Scholar 

  29. Hoogman M, Bralten J, Hibar DP, Mennes M, Zwiers MP, Schweren LSJ, et al. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry. 2017;4:310–9.

    Google Scholar 

  30. Barker ED, Ing A, Biondo F, Jia T, Pingault JB, Du Rietz E, et al. Do ADHD-impulsivity and BMI have shared polygenic and neural correlates? Mol Psychiatry. 2021;26:1019–28.

    CAS  Google Scholar 

  31. Cui Z, Li H, Xia CH, Larsen B, Adebimpe A, Baum GL, et al. Individual variation in functional topography of association networks in youth. Neuron. 2020;106:340–53.

    CAS  Google Scholar 

  32. Kochunov P, Coyle TR, Rowland LM, Jahanshad N, Thompson PM, Kelly S, et al. Association of white matter with core cognitive deficits in patients with schizophrenia. JAMA Psychiatry. 2017;74:958–66.

    Google Scholar 

  33. Winkelbeiner S, Leucht S, Kane JM, Homan P. Evaluation of differences in individual treatment response in schizophrenia spectrum disorders: a meta-analysis. JAMA Psychiatry. 2019;76:1063–73.

    Google Scholar 

  34. Maller JJ, Broadhouse K, Rush AJ, Gordon E, Koslow S, Grieve SM. Increased hippocampal tail volume predicts depression status and remission to anti-depressant medications in major depression. Mol Psychiatry. 2018;23:1737–44.

    CAS  Google Scholar 

  35. Siroux V, Agier L, Slama R. The exposome concept: a challenge and a potential driver for environmental health research. Eur Respir Rev. 2016;25:124–9.

    Google Scholar 

  36. Guxens M, Lubczynska MJ, Muetzel RL, Dalmau-Bueno A, Jaddoe VWV, Hoek G, et al. Air pollution exposure during fetal life, brain morphology, and cognitive function in school-age children. Biol Psychiatry. 2018;84:295–303.

    CAS  Google Scholar 

  37. Gale SD, Erickson LD, Anderson JE, Brown BL, Hedges DW. Association between exposure to air pollution and prefrontal cortical volume in adults: a cross-sectional study from the UK biobank. Environ Res. 2020;185:109365.

    CAS  Google Scholar 

  38. Power MC, Lamichhane AP, Liao D, Xu X, Jack CR, Gottesman RF, et al. The association of long-term exposure to particulate matter air pollution with brain MRI findings: the ARIC study. Environ Health Perspect. 2018;126:027009.

    Google Scholar 

  39. Lubczynska MJ, Muetzel RL, El Marroun H, Basagana X, Strak M, Denault W, et al. Exposure to air pollution during pregnancy and childhood, and white matter microstructure in preadolescents. Environ Health Perspect. 2020;128:27005.

    CAS  Google Scholar 

  40. Marshall AT, Betts S, Kan EC, McConnell R, Lanphear BP, Sowell ER. Association of lead-exposure risk and family income with childhood brain outcomes. Nat Med. 2020;26:91–7.

    CAS  Google Scholar 

  41. Margolis AE, Banker S, Pagliaccio D, De Water E, Curtin P, Bonilla A, et al. Functional connectivity of the reading network is associated with prenatal polybrominated diphenyl ether concentrations in a community sample of 5 year-old children: a preliminary study. Environ Int. 2020;134:105212.

    CAS  Google Scholar 

  42. Dadvand P, Pujol J, Macia D, Martinez-Vilavella G, Blanco-Hinojo L, Mortamais M, et al. The association between lifelong greenspace exposure and 3-dimensional brain magnetic resonance imaging in Barcelona schoolchildren. Environ Health Perspect. 2018;126:027012.

    Google Scholar 

  43. Tost H, Reichert M, Braun U, Reinhard I, Peters R, Lautenbach S, et al. Neural correlates of individual differences in affective benefit of real-life urban green space exposure. Nat Neurosci. 2019;22:1389–93.

    CAS  Google Scholar 

  44. Noble KG, Houston SM, Brito NH, Bartsch H, Kan E, Kuperman JM, et al. Family income, parental education and brain structure in children and adolescents. Nat Neurosci. 2015;18:773–8.

    CAS  Google Scholar 

  45. Gur RE, Moore TM, Rosen AFG, Barzilay R, Roalf DR, Calkins ME, et al. Burden of environmental adversity associated with psychopathology, maturation, and brain behavior parameters in youths. JAMA Psychiatry. 2019;76:966–75.

    Google Scholar 

  46. Tooley UA, Mackey AP, Ciric R, Ruparel K, Moore TM, Gur RC, et al. Associations between neighborhood SES and functional brain network development. Cereb Cortex. 2020;30:1–19.

    Google Scholar 

  47. Lederbogen F, Kirsch P, Haddad L, Streit F, Tost H, Schuch P, et al. City living and urban upbringing affect neural social stress processing in humans. Nature. 2011;474:498–501.

    CAS  Google Scholar 

  48. Xu J, Liu X, Li Q, Goldblatt R, Qin W, Liu F, et al. Global urbanicity is associated with brain and behaviour in young people. Nat Hum Behav. 2022;6:279–93.

    Google Scholar 

  49. Quinlan EB, Barker ED, Luo Q, Banaschewski T, Bokde ALW, Bromberg U, et al. Peer victimization and its impact on adolescent brain development and psychopathology. Mol Psychiatry. 2020;25:3066–76.

    CAS  Google Scholar 

  50. Quinlan EB, Cattrell A, Jia T, Artiges E, Banaschewski T, Barker G, et al. Psychosocial stress and brain function in adolescent psychopathology. Am J Psychiatry. 2017;174:785–94.

    Google Scholar 

  51. Chung MK, Buck Louis GM, Kannan K, Patel CJ. Exposome-wide association study of semen quality: systematic discovery of endocrine disrupting chemical biomarkers in fertility require large sample sizes. Environ Int. 2019;125:505–14.

    CAS  Google Scholar 

  52. Braun JM, Kalloo G, Kingsley SL, Li N. Using phenome-wide association studies to examine the effect of environmental exposures on human health. Environ Int. 2019;130:104877.

    CAS  Google Scholar 

  53. Zhuang X, Yang Z, Cordes D. A technical review of canonical correlation analysis for neuroscience applications. Hum Brain Mapp. 2020;41:3807–33.

    Google Scholar 

  54. Marco EM, Macri S, Laviola G. Critical age windows for neurodevelopmental psychiatric disorders: evidence from animal models. Neurotox Res. 2011;19:286–307.

    Google Scholar 

  55. Heyer DB, Meredith RM. Environmental toxicology: sensitive periods of development and neurodevelopmental disorders. Neurotoxicology. 2017;58:23–41.

    CAS  Google Scholar 

  56. Wu X, Dong H, Luo L, Zhu Y, Peng G, Reveille JD, et al. A novel statistic for genome-wide interaction analysis. PLoS Genet. 2010;6:e1001131.

    Google Scholar 

  57. Thomas D. Gene-environment-wide association studies: emerging approaches. Nat Rev Genet. 2010;11:259–72.

    CAS  Google Scholar 

  58. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–9.

    CAS  Google Scholar 

  59. Xu Q, Guo L, Cheng J, Wang M, Geng Z, Zhu W, et al. CHIMGEN: a Chinese imaging genetics cohort to enhance cross-ethnic and cross-geographic brain research. Mol Psychiatry. 2020;25:517–29.

    Google Scholar 

  60. Jernigan TL, Brown SA. Introduction. Dev Cogn Neurosci. 2018;32:1–3.

    Google Scholar 

  61. Zhang Y, Vaidya N, Iyengar U, Sharma E, Holla B, Ahuja CK, et al. The Consortium on Vulnerability to Externalizing Disorders and Addictions (c-VEDA): an accelerated longitudinal cohort of children and adolescents in India. Mol Psychiatry. 2020;25:1618–30.

  62. Schumann G, Loth E, Banaschewski T, Barbot A, Barker G, Buchel C, et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol Psychiatry. 2010;15:1128–39.

    CAS  Google Scholar 

  63. Kooijman MN, Kruithof CJ, van Duijn CM, Duijts L, Franco OH, van IMH, et al. The Generation R Study: design and cohort update 2017. Eur J Epidemiol. 2016;31:1243–64.

    Google Scholar 

  64. DeBord DG, Carreon T, Lentz TJ, Middendorf PJ, Hoover MD, Schulte PA. Use of the “exposome” in the practice of epidemiology: a primer on -omic technologies. Am J Epidemiol. 2016;184:302–14.

    Google Scholar 

  65. Stingone JA, Buck Louis GM, Nakayama SF, Vermeulen RC, Kwok RK, Cui Y, et al. Toward greater implementation of the exposome research paradigm within environmental epidemiology. Annu Rev Public Health. 2017;38:315–27.

    Google Scholar 

  66. Vineis P, Robinson O, Chadeau-Hyam M, Dehghan A, Mudway I, Dagnino S. What is new in the exposome? Environ Int. 2020;143:105887.

    Google Scholar 

  67. Turner MC, Nieuwenhuijsen M, Anderson K, Balshaw D, Cui Y, Dunton G, et al. Assessing the exposome with external measures: commentary on the state of the science and research recommendations. Annu Rev Public Health. 2017;38:215–39.

    Google Scholar 

  68. Sorek-Hamer M, Just AC, Kloog I. Satellite remote sensing in epidemiological studies. Curr Opin Pediatr. 2016;28:228–34.

    Google Scholar 

  69. Jiang C, Wang X, Li X, Inlora J, Wang T, Liu Q, et al. Dynamic human environmental exposome revealed by longitudinal personal monitoring. Cell. 2018;175:277–91.e31.

    CAS  Google Scholar 

  70. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4:1–32.

    Google Scholar 

  71. Thomas MC, Kamarck TW, Li X, Erickson KI, Manuck SB. Physical activity moderates the effects of daily psychosocial stressors on ambulatory blood pressure. Health Psychol. 2019;38:925–35.

    Google Scholar 

  72. Wulder MA, Loveland TR, Roy DP, Crawford CJ, Masek JG, Woodcock CE, et al. Current status of Landsat program, science, and applications. Remote Sens Environ. 2019;225:127–47.

    Google Scholar 

  73. Maitre L, de Bont J, Casas M, Robinson O, Aasvang GM, Agier L, et al. Human Early Life Exposome (HELIX) study: a European population-based exposome cohort. BMJ Open. 2018;8:e021311.

    Google Scholar 

  74. Keogh RH, Shaw PA, Gustafson P, Carroll RJ, Deffner V, Dodd KW, et al. STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: part 1-basic theory and simple methods of adjustment. Stat Med. 2020;39:2197–231.

    Google Scholar 

  75. Shaw PA, Gustafson P, Carroll RJ, Deffner V, Dodd KW, Keogh RH, et al. STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: part 2-more complex methods of adjustment and advanced topics. Stat Med. 2020;39:2232–63.

    Google Scholar 

  76. Lou W, Wan L, Abner EL, Fardo DW, Dodge HH, Kryscio RJ. Multi-state models and missing covariate data: Expectation-Maximization algorithm for likelihood estimation. Biostat Epidemiol. 2017;1:20–35.

    Google Scholar 

  77. Tanner MA, Wong WH. From EM to data augmentation: the emergence of MCMC Bayesian computation in the 1980s. Stat Sci. 2010;25:506–16.

    Google Scholar 

  78. Buuren SV, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45:1–68.

    Google Scholar 

  79. Enders CK, Mistler SA, Keller BT. Multilevel multiple imputation: a review and evaluation of joint modeling and chained equations imputation. Psychol Methods. 2016;21:222–40.

    Google Scholar 

  80. Sheikh K. Investigation of selection bias using inverse probability weighting. Eur J Epidemiol. 2007;22:349–50.

    Google Scholar 

  81. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27.

    Google Scholar 

  82. Nygaard V, Rødland EA, Hovig E. Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics. 2016;17:29–39.

    Google Scholar 

  83. Zindler T, Frieling H, Neyazi A, Bleich S, Friedel E. Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies. BMC Bioinform. 2020;21:271.

    CAS  Google Scholar 

  84. Yamashita A, Yahata N, Itahashi T, Lisi G, Yamada T, Ichikawa N, et al. Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias. PLoS Biol. 2019;17:e3000042.

    CAS  Google Scholar 

  85. Zhong J, Wang Y, Li J, Xue X, Liu S, Wang M, et al. Inter-site harmonization based on dual generative adversarial networks for diffusion tensor imaging: application to neonatal white matter development. Biomed Eng Online. 2020;19:4.

    Google Scholar 

  86. Wang D, Buckner RL, Fox MD, Holt DJ, Holmes AJ, Stoecklein S, et al. Parcellating cortical functional networks in individuals. Nat Neurosci. 2015;18:1853–60.

    CAS  Google Scholar 

  87. Ren J, Xu T, Wang D, Li M, Lin Y, Schoeppe F, et al. Individual variability in functional organization of the human and monkey auditory cortex. Cereb Cortex. 2021;31:2450–65.

    Google Scholar 

  88. Liu Z, Palaniyappan L, Wu X, Zhang K, Du J, Zhao Q, et al. Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis. Mol Psychiatry. 2021;26:7719–31.

    Google Scholar 

  89. Li J, Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity. 2005;95:221–7.

    CAS  Google Scholar 

  90. Bottolo L, Chadeau-Hyam M, Hastie DI, Zeller T, Liquet B, Newcombe P, et al. GUESS-ing polygenic associations with multiple phenotypes using a GPU-based evolutionary stochastic search algorithm. PLoS Genet. 2013;9:e1003657.

    CAS  Google Scholar 

  91. Sinisi SE, van der Laan MJ Deletion/substitution/addition algorithm in learning with applications in genomics. Stat Appl Genet Mol Biol 2004;3:Article18.

  92. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67:301–20.

    Google Scholar 

  93. Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016;374:20150202.

    Google Scholar 

  94. Sellbom M, Tellegen A. Factor analysis in psychological assessment research: common pitfalls and recommendations. Psychol Assess. 2019;31:1428–41.

    Google Scholar 

  95. Chun H, Keles S. Sparse partial least squares regression for simultaneous dimension reduction and variable selection. J R Stat Soc Ser B Stat Methodol. 2010;72:3–25.

    Google Scholar 

  96. Carrico C, Gennings C, Wheeler DC, Factor-Litvak P. Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting. J Agric Biol Environ Stat. 2015;20:100–20.

    Google Scholar 

  97. Bobb JF, Valeri L, Claus Henn B, Christiani DC, Wright RO, Mazumdar M, et al. Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Biostatistics. 2015;16:493–508.

    Google Scholar 

  98. Keil AP, Buckley JP, O’Brien KM, Ferguson KK, Zhao S, White AJ. A quantile-based g-computation approach to addressing the effects of exposure mixtures. Environ Health Perspect. 2020;128:47004.

    Google Scholar 

  99. Mihalik A, Adams RA, Huys Q. Canonical correlation analysis for identifying biotypes of depression. Biol Psychiatry Cogn Neurosci Neuroimaging. 2020;5:478–80.

    Google Scholar 

  100. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey, Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27:1133–63.

    Google Scholar 

  101. Liu SH, Ulbricht CM, Chrysanthopoulou SA, Lapane KL. Implementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies. BMC Res Notes. 2016;9:354.

    Google Scholar 

  102. Baccini M, Mattei A, Mealli F, Bertazzi PA, Carugno M. Assessing the short term impact of air pollution on mortality: a matching approach. Environ Health. 2017;16:7.

    Google Scholar 

  103. Robins J. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Math Model. 1986;7:1393–512.

    Google Scholar 

  104. Gasparrini A. Distributed lag linear and non-linear models in R: the package dlnm. J Stat Softw. 2011;43:1–20.

    Google Scholar 

  105. Gauderman WJ, Mukherjee B, Aschard H, Hsu L, Lewinger JP, Patel CJ, et al. Update on the state of the science for analytical methods for gene-environment interactions. Am J Epidemiol. 2017;186:762–70.

    Google Scholar 

  106. Pare G, Cook NR, Ridker PM, Chasman DI. On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the Women’s Genome Health Study. PLoS Genet. 2010;6:e1000981.

    Google Scholar 

  107. Ritchie MD, Davis JR, Aschard H, Battle A, Conti D, Du M, et al. Incorporation of biological knowledge into the study of gene-environment interactions. Am J Epidemiol. 2017;186:771–7.

    Google Scholar 

  108. Moore R, Casale FP, Jan Bonder M, Horta D, Franke L, Barroso I, et al. A linear mixed-model approach to study multivariate gene-environment interactions. Nat Genet. 2019;51:180–6.

    CAS  Google Scholar 

  109. Gola D, Mahachie John JM, van Steen K, Konig IR. A roadmap to multifactor dimensionality reduction methods. Brief Bioinform. 2016;17:293–308.

    Google Scholar 

  110. Jakulin A. Machine learning based on attribute interactions. Ph.D. Dissertation. University of Ljubljana; 2005.

  111. Ignac TM, Skupin A, Sakhanenko NA, Galas DJ. Discovering pair-wise genetic interactions: an information theory-based approach. PLoS ONE. 2014;9:e92310.

    Google Scholar 

  112. Sun Z, Tao Y, Li S, Ferguson KK, Meeker JD, Park SK, et al. Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons. Environ Health. 2013;12:85.

    Google Scholar 

  113. Davalos AD, Luben TJ, Herring AH, Sacks JD. Current approaches used in epidemiologic studies to examine short-term multipollutant air pollution exposures. Ann Epidemiol. 2017;27:145–53.e1.

    Google Scholar 

  114. Osborne J. Improving your data transformations: applying the Box-Cox transformation. Practical Assessment. Pract Assess Res Eval. 2010;15:12.

    Google Scholar 

  115. Abdi H, Williams LJ (2010). Normalizing data. In: Salkind NJ, editor. Encyclopedia of research design. Thousand Oaks, CA: Sage; 2010:935–8.

  116. Qiu X, Wu H, Hu R. The impact of quantile and rank normalization procedures on the testing power of gene differential expression analysis. BMC Bioinform. 2013;14:124.

    Google Scholar 

  117. Bacher R, Chu LF, Leng N, Gasch AP, Thomson JA, Stewart RM, et al. SCnorm: robust normalization of single-cell RNA-seq data. Nat Methods. 2017;14:584–6.

    CAS  Google Scholar 

  118. Shaw PA, Deffner V, Keogh RH, Tooze JA, Dodd KW, Kuchenhoff H, et al. Epidemiologic analyses with error-prone exposures: review of current practice and recommendations. Ann Epidemiol. 2018;28:821–8.

    Google Scholar 

  119. Elvidge CD, Baugh K, Kihn E, Kroehl HW, Davis E. Mapping city lights with nighttime data from the DMSP Operational Linescan System. Photogramm Eng Remote Sens. 1997;63:727–34.

    Google Scholar 

  120. ESA (European Space Agency). Land cover CCI product user guide version 2. Technical report. ESA: 2017.

  121. Van Donkelaar A, Martin RV, Brauer M, Hsu NC, Kahn RA, Levy RC, et al. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environ Sci Technol. 2016;50:3762–72.

    Google Scholar 

  122. Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci Data. 2018;5:170191.

    Google Scholar 

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Acknowledgements

This work was partly supported by the National Key Research and Development Program of China (Grant No. 2018YFC1314301), the National Natural Science Foundation of China (Grant Nos. 82030053, 82072001, 82001797, 81971694, 81971599), and Tianjin Natural Science Foundation (19JCYBJC25100). Further support was received by GS from the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network-based stratification of reinforcement-related disorders) (695313), the National Institute of Health (NIH) (R01DA049238, A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the Human Brain Project (SGA3; 945539), the EC Horizon Europe Project ‘environMENTAL’ (101057429), the German Research Foundation (DFG) grant COPE (458317126), the Chinese National High-end Foreign Expert Recruitment Plan and the NSFC Research Award for International Senior Scientists 2021. We also thank Le Yu (Tsinghua University), Mingming Jia (Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences), and Shiwei Li (PIESAT Information Technology Co., Ltd) for the guidance of satellite-based remote sensing data processing, Meichen Yu (Indiana University School of Medicine) for the discussion of data harmonization, and Mengge Liu (Tianjin Medical University) for the support of graphic presentation.

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Author notes

  1. These authors contributed equally: Feng Liu, Jiayuan Xu, Lining Guo.

Authors and Affiliations

  1. Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, PR China

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

  2. School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, 300203, PR China

    Meng Liang

  3. Centre for Population Neuroscience and Stratified Medicine (PONS), Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, PR China

    Gunter Schumann

  4. Centre for Population Neuroscience and stratified Medicine (PONS), Klinik fuer Psychiatrie und Psychotherapie, Charite, CCM, Chariteplatz 1, Berlin, Germany

    Gunter Schumann

  5. CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, PR China

    Chunshui Yu

Authors
  1. Feng Liu
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  2. Jiayuan Xu
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  3. Lining Guo
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  4. Wen Qin
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Contributions

Conception, design and drafting manuscript: CY, FL, JX, LG and GS. Data analysis and interpretation: FL, LG, WQ and ML. All authors critically reviewed and approved the final version of the manuscript.

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Correspondence to Chunshui Yu.

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Liu, F., Xu, J., Guo, L. et al. Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior. Mol Psychiatry 28, 17–27 (2023). https://doi.org/10.1038/s41380-022-01669-6

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  • DOI: https://doi.org/10.1038/s41380-022-01669-6

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