Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits

Abstract

Exposure to preadult environmental exposures may have long-lasting effects on mental health by affecting the maturation of the brain and personality, two traits that interact throughout the developmental process. However, environment-brain-personality covariation patterns and their mediation relationships remain unclear. In 4297 healthy participants (aged 18–30 years), we combined sparse multiple canonical correlation analysis with independent component analysis to identify the three-way covariation patterns of 59 preadult environmental exposures, 760 adult brain imaging phenotypes, and five personality traits, and found two robust environment-brain-personality covariation models with sex specificity. One model linked greater stress and less support to weaker functional connectivity and activity in the default mode network, stronger activity in subcortical nuclei, greater thickness and volume in the occipital, parietal and temporal cortices, and lower agreeableness, consciousness and extraversion as well as higher neuroticism. The other model linked higher urbanicity and better socioeconomic status to stronger functional connectivity and activity in the sensorimotor network, smaller volume and surface area and weaker functional connectivity and activity in the medial prefrontal cortex, lower white matter integrity, and higher openness to experience. We also conducted mediation analyses to explore the potential bidirectional mediation relationships between adult brain imaging phenotypes and personality traits with the influence of preadult environmental exposures and found both environment-brain-personality and environment-personality-brain pathways. We finally performed moderated mediation analyses to test the potential interactions between macro- and microenvironmental exposures and found that one category of exposure moderated the mediation pathways of another category of exposure. These results improve our understanding of the effects of preadult environmental exposures on the adult brain and personality traits and may facilitate the design of targeted interventions to improve mental health by reducing the impact of adverse environmental exposures.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic summary of the study design.
Fig. 2: Two significant three-way covariation models.
Fig. 3: The contribution of brain imaging phenotypes to each model.
Fig. 4: The main contributors of each model.
Fig. 5: Mediation and moderated mediation analyses for significant models.

Similar content being viewed by others

Code availability

Structural MRI data for VBM and SBM analyses were preprocessed separately by CAT12 (version r1364, http://dbm.neuro.uni-jena.de/cat) and FreeSurfer (version 6.0.0) (http://surfer.nmr.mgh.harvard.edu/), respectively. DTI data were preprocessed by FSL 5.0.10 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). rs-fMRI data were preprocessed by SPM12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/) and Data Processing Assistant for Resting-State fMRI (http://rfmri.org/dpabi). Brain imaging phenotypes visualisation was done using MRIcroGL (https://www.nitrc.org/projects/mricrogl/) and ggseg package in R (https://github.com/ggseg/ggseg). The codes for smCCA are available on the bnaras GitHub (https://github.com/bnaras/PMA). The codes for FastICA are available at https://www.fmrib.ox.ac.uk/ukbiobank/nnpaper/ukb_NN.m.

References

  1. Xue T, Zhu T, Zheng Y, Zhang Q. Declines in mental health associated with air pollution and temperature variability in China. Nat Commun. 2019;10:2165.

    ADS  PubMed  PubMed Central  Google Scholar 

  2. Short AK, Baram TZ. Early-life adversity and neurological disease: age-old questions and novel answers. Nat Rev Neurol. 2019;15:657–69.

    PubMed  PubMed Central  Google Scholar 

  3. Miguel PM, Pereira LO, Silveira PP, Meaney MJ. Early environmental influences on the development of children’s brain structure and function. Dev Med Child Neurol. 2019;61:1127–33.

    PubMed  Google Scholar 

  4. Von Der Heide R, Vyas G, Olson IR. The social network-network: size is predicted by brain structure and function in the amygdala and paralimbic regions. Soc Cogn Affect Neurosci. 2014;9:1962–72.

    PubMed  Google Scholar 

  5. Lamblin M, Murawski C, Whittle S, Fornito A. Social connectedness, mental health and the adolescent brain. Neurosci Biobehav Rev. 2017;80:57–68.

    CAS  PubMed  Google Scholar 

  6. Moser DA, Doucet GE, Ing A, Dima D, Schumann G, Bilder RM, et al. An integrated brain-behavior model for working memory. Mol Psychiatry. 2018;23:1974–80.

    CAS  PubMed  Google Scholar 

  7. Modabbernia A, Reichenberg A, Ing A, Moser DA, Doucet GE, Artiges E, et al. Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study. Mol Psychiatry. 2021;26:4905–18.

    CAS  PubMed  Google Scholar 

  8. 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  PubMed  PubMed Central  Google Scholar 

  9. Tomasi D, Volkow ND. Associations of family income with cognition and brain structure in USA children: prevention implications. Mol Psychiatry. 2021;26:6619–29.

    PubMed  PubMed Central  Google Scholar 

  10. Modabbernia A, Janiri D, Doucet GE, Reichenberg A, Frangou S. Multivariate patterns of brain-behavior-environment associations in the adolescent brain and cognitive development study. Biol Psychiatry. 2021;89:510–20.

    PubMed  Google Scholar 

  11. Jeong HJ, Moore TM, Durham EL, Reimann GE, Dupont RM, Cardenas-Iniguez C, et al. General and specific factors of environmental stress and their associations with brain structure and dimensions of psychopathology. Biol Psychiatry: Glob Open Sci. 2022;3:480–9.

    PubMed  Google Scholar 

  12. Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E, Xu J, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016;19:1523–36.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Alnæs D, Kaufmann T, Marquand AF, Smith SM, Westlye LT. Patterns of sociocognitive stratification and perinatal risk in the child brain. Proc Natl Acad Sci USA. 2020;117:12419–27.

    ADS  PubMed  PubMed Central  Google Scholar 

  14. 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.

    PubMed  Google Scholar 

  15. Liu F, Xu J, Guo L, Qin W, Liang M, Schumann G, et al. Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior. Mol Psychiatry. 2023;28:17–27.

    CAS  PubMed  Google Scholar 

  16. Rosenzweig MR. Effects of differential experience on the brain and behavior. Dev Neuropsychol. 2003;24:523–40.

    PubMed  Google Scholar 

  17. Caspi A, Houts RM, Belsky DW, Goldman-Mellor SJ, Harrington H, Israel S, et al. The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clin Psychol Sci. 2014;2:119–37.

    PubMed  PubMed Central  Google Scholar 

  18. Bjørnebekk A, Fjell AM, Walhovd KB, Grydeland H, Torgersen S, Westlye LT. Neuronal correlates of the five factor model (FFM) of human personality: multimodal imaging in a large healthy sample. Neuroimage. 2013;65:194–208.

    PubMed  Google Scholar 

  19. Kolb B, Harker A, Gibb R. Principles of plasticity in the developing brain. Dev Med Child Neurol. 2017;59:1218–23.

    PubMed  Google Scholar 

  20. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc: Ser B (Methodol). 1977;39:1–22.

    MathSciNet  Google Scholar 

  21. Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage. 2018;167:104–20.

    PubMed  Google Scholar 

  22. Van Dijk KR, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage. 2012;59:431–8.

    PubMed  Google Scholar 

  23. Witten DM, Tibshirani R, Hastie T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics. 2009;10:515–34.

    PubMed  PubMed Central  Google Scholar 

  24. Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw. 1999;10:626–34.

    PubMed  Google Scholar 

  25. Tingley D, Yamamoto T, Hirose K, Keele L, Imai K. mediation: R Package for Causal Mediation Analysis. J Stat Softw. 2014;59:1–38.

  26. Hayes AF. Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. New York: Guilford Press; 2017.

  27. Moser DA, Doucet GE, Lee WH, Rasgon A, Krinsky H, Leibu E, et al. Multivariate associations among behavioral, clinical, and multimodal imaging phenotypes in patients with psychosis. JAMA Psychiatry. 2018;75:386–95.

    PubMed  PubMed Central  Google Scholar 

  28. Grant S, Langan-Fox J. Personality and the occupational stressor-strain relationship: the role of the Big Five. J Occup Health Psychol. 2007;12:20–33.

    PubMed  Google Scholar 

  29. Suls J, Martin R. The daily life of the garden-variety neurotic: reactivity, stressor exposure, mood spillover, and maladaptive coping. J Pers. 2005;73:1485–509.

    PubMed  Google Scholar 

  30. Kendler KS, Myers J. The genetic and environmental relationship between major depression and the five-factor model of personality. Psychol Med. 2010;40:801–6.

    CAS  PubMed  Google Scholar 

  31. Canli T. Biology of personality and individual differences. New York: Guilford Press; 2006.

  32. Dube SR, Felitti VJ, Dong M, Chapman DP, Giles WH, Anda RF. Childhood abuse, neglect, and household dysfunction and the risk of illicit drug use: the adverse childhood experiences study. Pediatrics. 2003;111:564–72.

    PubMed  Google Scholar 

  33. Hayes JF, Osborn DPJ, Lewis G, Dalman C, Lundin A. Association of late adolescent personality with risk for subsequent serious mental illness among men in a Swedish nationwide cohort study. JAMA Psychiatry. 2017;74:703–11.

    PubMed  PubMed Central  Google Scholar 

  34. Shonkoff JP. Leveraging the biology of adversity to address the roots of disparities in health and development. Proc Natl Acad Sci USA. 2012;109:17302–7.

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Santesteban-Echarri O, MacQueen G, Goldstein BI, Wang J, Kennedy SH, Bray S, et al. Personality and risk for serious mental illness. Early Inter Psychiatry. 2021;15:133–9.

    Google Scholar 

  36. Akiki TJ, Averill CL, Wrocklage KM, Scott JC, Averill LA, Schweinsburg B, et al. Default mode network abnormalities in posttraumatic stress disorder: a novel network-restricted topology approach. Neuroimage. 2018;176:489–98.

    PubMed  Google Scholar 

  37. Mohiyeddini C, Bauer S, Semple S. Neuroticism and stress: the role of displacement behavior. Anxiety Stress Coping. 2015;28:391–407.

    PubMed  Google Scholar 

  38. Blain SD, Grazioplene RG, Ma Y, DeYoung CG. Toward a neural model of the openness-psychoticism dimension: functional connectivity in the default and frontoparietal control networks. Schizophr Bull. 2020;46:540–51.

    PubMed  Google Scholar 

  39. Rauch SL, Shin LM, Phelps EA. Neurocircuitry models of posttraumatic stress disorder and extinction: human neuroimaging research–past, present, and future. Biol Psychiatry. 2006;60:376–82.

    PubMed  Google Scholar 

  40. Liu WZ, Zhang WH, Zheng ZH, Zou JX, Liu XX, Huang SH, et al. Identification of a prefrontal cortex-to-amygdala pathway for chronic stress-induced anxiety. Nat Commun. 2020;11:2221.

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  41. 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  PubMed  Google Scholar 

  42. Disner SG, Beevers CG, Haigh EA, Beck AT. Neural mechanisms of the cognitive model of depression. Nat Rev Neurosci. 2011;12:467–77.

    CAS  PubMed  Google Scholar 

  43. Callaghan BL, Tottenham N. The stress acceleration hypothesis: effects of early-life adversity on emotion circuits and behavior. Curr Opin Behav Sci. 2016;7:76–81.

    PubMed  Google Scholar 

  44. Frankenhuis WE, de Weerth C. Does early-life exposure to stress shape or impair cognition? Curr Dir Psychol Sci. 2013;22:407–12.

    Google Scholar 

  45. Pervin LA. The science of personality. New York: Oxford University Press; 2003.

  46. DeYoung CG, Quilty LC, Peterson JB, Gray JR. Openness to experience, intellect, and cognitive ability. J Pers Assess. 2014;96:46–52.

    PubMed  Google Scholar 

  47. Streit F, Witt SH, Awasthi S, Foo JC, Jungkunz M, Frank J, et al. Borderline personality disorder and the big five: molecular genetic analyses indicate shared genetic architecture with neuroticism and openness. Transl Psychiatry. 2022;12:153.

    PubMed  PubMed Central  Google Scholar 

  48. Kemp KC, Burgin CJ, Raulin ML, Kwapil TR. Using multiple measures of openness to experience to capture positive, negative, and disorganized dimensions of schizotypy. Personal Disord. 2020;11:260–9.

    PubMed  Google Scholar 

  49. Zhou Y, Li D, Li X, Wang Y, Zhao L. Big five personality and adolescent Internet addiction: The mediating role of coping style. Addict Behav. 2017;64:42–8.

    PubMed  Google Scholar 

  50. Shi B, Dai DY, Lu Y. Openness to experience as a moderator of the relationship between intelligence and creative thinking: a study of chinese children in urban and rural areas. Front Psychol. 2016;7:641.

    PubMed  PubMed Central  Google Scholar 

  51. Jonassaint CR, Siegler IC, Barefoot JC, Edwards CL, Williams RB. Low life course socioeconomic status (SES) is associated with negative NEO PI-R personality patterns. Int J Behav Med. 2011;18:13–21.

    PubMed  PubMed Central  Google Scholar 

  52. Delgado MR, Beer JS, Fellows LK, Huettel SA, Platt ML, Quirk GJ, et al. Viewpoints: dialogues on the functional role of the ventromedial prefrontal cortex. Nat Neurosci. 2016;19:1545–52.

    CAS  PubMed  Google Scholar 

  53. 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.

    PubMed  Google Scholar 

  54. Mulders P, Llera A, Tendolkar I, van Eijndhoven P, Beckmann C. Personality profiles are associated with functional brain networks related to cognition and emotion. Sci Rep. 2018;8:13874.

    ADS  PubMed  PubMed Central  Google Scholar 

  55. Thase ME, Wright JH, Friedman ES, Russ E. Cognitive and behavioral therapies. Psychiatry. 2015;1:1836–58.

    Google Scholar 

  56. Wang X, Zhuang K, Li Z, Qiu J. The functional connectivity basis of creative achievement linked with openness to experience and divergent thinking. Biol Psychol. 2022;168:108260.

    PubMed  Google Scholar 

  57. Kappes HB, Morewedge CK. Mental simulation as substitute for experience. Soc Personal Psychol Compass. 2016;10:405–20.

    Google Scholar 

  58. Minuzzi L, Syan SK, Smith M, Hall A, Hall GB, Frey BN. Structural and functional changes in the somatosensory cortex in euthymic females with bipolar disorder. Aust N Z J Psychiatry. 2018;52:1075–83.

    PubMed  Google Scholar 

  59. Schmaal L, Hibar DP, Sämann 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  PubMed  Google Scholar 

  60. Woodward ND, Karbasforoushan H, Heckers S. Thalamocortical dysconnectivity in schizophrenia. Am J Psychiatry. 2012;169:1092–9.

    PubMed  Google Scholar 

  61. Zhu X, Yan W, Lin X, Que J, Huang Y, Zheng H, et al. The effect of perceived stress on cognition is mediated by personality and the underlying neural mechanism. Transl Psychiatry. 2022;12:199.

    PubMed  PubMed Central  Google Scholar 

  62. Mackey S, Chaarani B, Kan KJ, Spechler PA, Orr C, Banaschewski T, et al. Brain regions related to impulsivity mediate the effects of early adversity on antisocial behavior. Biol Psychiatry. 2017;82:275–82.

    PubMed  Google Scholar 

  63. Dubois J, Eberhardt F, Paul LK, Adolphs R. Personality beyond taxonomy. Nat Hum Behav. 2020;4:1110–7.

    PubMed  Google Scholar 

  64. van den Bosch M, Meyer-Lindenberg A. Environmental exposures and depression: biological mechanisms and epidemiological evidence. Annu Rev Public Health. 2019;40:239–59.

    PubMed  Google Scholar 

  65. Bale TL, Epperson CN. Sex differences and stress across the lifespan. Nat Neurosci. 2015;18:1413–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Heim C, Shugart M, Craighead WE, Nemeroff CB. Neurobiological and psychiatric consequences of child abuse and neglect. Dev Psychobiol. 2010;52:671–90.

    PubMed  Google Scholar 

Download references

Acknowledgements

This work was funded by Natural Science Foundation of China (82030053), Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-001A) and Tsinghua-Toyota Joint Research Fund. No other disclosures were reported.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

KX, WS, YM, and CY conceptualised the study. KX conducted the analysis, and wrote the first version of the manuscript. BG, FC helped analyse data and revise the manuscript. MW, JC, BZ, WZ, SQ, XZ, ZG, GC, YY, WL, HZ, XX, TH, WQ, FL, ML, LG, QX, JX, JF, PZ, WL, DS, CW, ZY, JZ, JL, DW, JX, KX, X-NZ, LZ, ZY, WS, YM, and CY collected imaging data. TB, GJB, ALWB, SD, HF, AG, HG, PG, AH, RB, J-LM, M-LPM, EA, FN, DPO, HL, LP, SH, NH, JHF, MNS, NV, HW and RW acquired the data. WS, YM, and CY provided critical comments on the manuscript and approved the final version of the manuscript. All authors contributed to the manuscript. The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Wen Shen, Yanwei Miao or Chunshui Yu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xue, K., Gao, B., Chen, F. et al. Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits. Mol Psychiatry 28, 4853–4866 (2023). https://doi.org/10.1038/s41380-023-02261-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-023-02261-2

This article is cited by

Search

Quick links