IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-58785-4.html
   My bibliography  Save this article

Perinatal SSRI exposure impacts innate fear circuit activation and behavior in mice and humans

Author

Listed:
  • Giulia Zanni

    (Columbia University
    New York State Psychiatric Institute)

  • Milenna T. Dijk

    (Columbia University
    New York State Psychiatric Institute)

  • Martha Caffrey Cagliostro

    (Columbia University
    Northeastern University)

  • Pradyumna Sepulveda

    (Columbia University)

  • Nicolò Pini

    (Columbia University
    New York State Psychiatric Institute)

  • Ariel L. Rose

    (Columbia University
    New York State Psychiatric Institute)

  • Alexander L. Kesin

    (Columbia University
    New York State Psychiatric Institute)

  • Claudia Lugo-Candelas

    (Columbia University
    New York State Psychiatric Institute)

  • Priscila Dib Goncalves

    (Columbia University)

  • Alexandra S. MacKay

    (Columbia University
    New York State Psychiatric Institute)

  • Kiyohito Iigaya

    (Columbia University
    New York State Psychiatric Institute
    Columbia University
    Columbia University)

  • Praveen Kulkarni

    (Northeastern University)

  • Craig F. Ferris

    (Northeastern University)

  • Myrna M. Weissman

    (Columbia University
    New York State Psychiatric Institute
    Columbia University)

  • Ardesheer Talati

    (Columbia University
    New York State Psychiatric Institute)

  • Mark S. Ansorge

    (Columbia University
    New York State Psychiatric Institute)

  • Jay A. Gingrich

    (Columbia University
    New York State Psychiatric Institute)

Abstract

Before assuming its role in the mature brain, serotonin modulates early brain development across phylogenetically diverse species. In mice and humans, early-life SSRI exposure alters the offspring’s brain structure and is associated with anxiety and depression-related behaviors beginning in puberty. However, the impact of early-life SSRI exposure on brain circuit function is unknown. To address this question, we examined how developmental SSRI exposure changes fear-related brain activation and behavior in mice and humans. SSRI-exposed mice showed increased defense responses to a predator odor, and stronger fMRI amygdala and extended fear-circuit activation. Likewise, adolescents exposed to SSRIs in utero exhibited higher anxiety and depression symptoms than unexposed adolescents and also had greater activation of the amygdala and other limbic structures when processing fearful faces. These findings demonstrate that increases in anxiety and fear-related behaviors as well as brain circuit activation following developmental SSRI exposure are conserved between mice and humans. These findings have potential implications for the clinical use of SSRIs during human pregnancy and for designing interventions that protect fetal brain development.

Suggested Citation

  • Giulia Zanni & Milenna T. Dijk & Martha Caffrey Cagliostro & Pradyumna Sepulveda & Nicolò Pini & Ariel L. Rose & Alexander L. Kesin & Claudia Lugo-Candelas & Priscila Dib Goncalves & Alexandra S. MacK, 2025. "Perinatal SSRI exposure impacts innate fear circuit activation and behavior in mice and humans," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58785-4
    DOI: 10.1038/s41467-025-58785-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-58785-4
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-58785-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Christopher Genovese & Larry Wasserman, 2002. "Operating characteristics and extensions of the false discovery rate procedure," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 499-517, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. B. Moerkerke & E. Goetghebeur & J. De Riek & I. Roldán‐Ruiz, 2006. "Significance and impotence: towards a balanced view of the null and the alternative hypotheses in marker selection for plant breeding," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 61-79, January.
    2. Gómez-Villegas Miguel A. & Salazar Isabel & Sanz Luis, 2014. "A Bayesian decision procedure for testing multiple hypotheses in DNA microarray experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(1), pages 49-65, February.
    3. Arianna Dondi & Jacopo Lenzi & Egidio Candela & Sugitha Sureshkumar & Francesca Morigi & Carlotta Biagi & Marcello Lanari, 2022. "Social Determinants of Health and Distance Learning in Italy in the Era of the SARS-CoV-2 Pandemic," IJERPH, MDPI, vol. 19(9), pages 1-12, May.
    4. Long Qu & Dan Nettleton & Jack C. M. Dekkers, 2012. "Improved Estimation of the Noncentrality Parameter Distribution from a Large Number of t-Statistics, with Applications to False Discovery Rate Estimation in Microarray Data Analysis," Biometrics, The International Biometric Society, vol. 68(4), pages 1178-1187, December.
    5. Jiangzhou Wang & Pengfei Wang, 2024. "Large-scale dependent multiple testing via hidden semi-Markov models," Computational Statistics, Springer, vol. 39(3), pages 1093-1126, May.
    6. Anti, Sebastian, 2021. "Land grabs and labor in Cambodia," Journal of Development Economics, Elsevier, vol. 149(C).
    7. Cipolli III, William & Hanson, Timothy & McLain, Alexander C., 2016. "Bayesian nonparametric multiple testing," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 64-79.
    8. Dazard, Jean-Eudes & Sunil Rao, J., 2012. "Joint adaptive mean–variance regularization and variance stabilization of high dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2317-2333.
    9. Zehetmayer Sonja & Graf Alexandra C. & Posch Martin, 2015. "Sample size reassessment for a two-stage design controlling the false discovery rate," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(5), pages 429-442, November.
    10. John Deke, "undated". "Causal Validity Considerations for Including High Quality Non-Experimental Evidence in Systematic Reviews," Mathematica Policy Research Reports 676a04feb19e4904a052ba2e7, Mathematica Policy Research.
    11. Michael A. Langston & Robert S. Levine & Barbara J. Kilbourne & Gary L. Rogers & Anne D. Kershenbaum & Suzanne H. Baktash & Steven S. Coughlin & Arnold M. Saxton & Vincent K. Agboto & Darryl B. Hood &, 2014. "Scalable Combinatorial Tools for Health Disparities Research," IJERPH, MDPI, vol. 11(10), pages 1-25, October.
    12. Yongqiang Tang & Subhashis Ghosal & Anindya Roy, 2007. "Nonparametric Bayesian Estimation of Positive False Discovery Rates," Biometrics, The International Biometric Society, vol. 63(4), pages 1126-1134, December.
    13. Habiger, Joshua D. & Adekpedjou, Akim, 2014. "Optimal rejection curves for exact false discovery rate control," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 21-28.
    14. David R. Bickel, 2013. "Minimax-Optimal Strength of Statistical Evidence for a Composite Alternative Hypothesis," International Statistical Review, International Statistical Institute, vol. 81(2), pages 188-206, August.
    15. Yoav Benjamini, 2010. "Discovering the false discovery rate," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 405-416, September.
    16. Hai Shu & Bin Nan & Robert Koeppe, 2015. "Multiple testing for neuroimaging via hidden Markov random field," Biometrics, The International Biometric Society, vol. 71(3), pages 741-750, September.
    17. Guo, Wenge & Bhaskara Rao, M., 2008. "On optimality of the Benjamini-Hochberg procedure for the false discovery rate," Statistics & Probability Letters, Elsevier, vol. 78(14), pages 2024-2030, October.
    18. repec:plo:pone00:0158216 is not listed on IDEAS
    19. Elisa C. J. Maria & Isabel Salazar & Luis Sanz & Miguel A. Gómez-Villegas, 2020. "Using Copula to Model Dependence When Testing Multiple Hypotheses in DNA Microarray Experiments: A Bayesian Approximation," Mathematics, MDPI, vol. 8(9), pages 1-22, September.
    20. Dillon Bowen, 2022. "Bayesian ranking and selection with applications to field studies, economic mobility, and forecasting," Papers 2208.02038, arXiv.org.
    21. Chen, Xiongzhi & Doerge, R.W., 2020. "A strong law of large numbers related to multiple testing normal means," Statistics & Probability Letters, Elsevier, vol. 159(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58785-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.