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Convergent and divergent brain–cognition development in early adolescence
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  • Published: 26 May 2026

Convergent and divergent brain–cognition development in early adolescence

  • Yapei Xie  ORCID: orcid.org/0000-0003-3590-75021,2,3,4,
  • Shaoshi Zhang  ORCID: orcid.org/0000-0002-6352-91501,2,3,4,5,
  • Csaba Orban  ORCID: orcid.org/0000-0001-9133-35611,2,3,4,
  • Leon Qi Rong Ooi  ORCID: orcid.org/0000-0002-3546-45801,2,3,4,5,
  • Ru Kong  ORCID: orcid.org/0000-0001-7842-03291,2,3,4,
  • Dorothea L. Floris  ORCID: orcid.org/0000-0001-5838-68216,7,
  • Xi-Nian Zuo  ORCID: orcid.org/0000-0001-9110-585X8,9,10,
  • Elvisha Dhamala  ORCID: orcid.org/0000-0002-8253-696211,12,13,
  • Avram J. Holmes14,
  • Lucina Q. Uddin  ORCID: orcid.org/0000-0003-2278-896215,
  • Thomas E. Nichols  ORCID: orcid.org/0000-0002-4516-510316,
  • Adriana Di Martino17 &
  • …
  • B. T. Thomas Yeo  ORCID: orcid.org/0000-0002-0119-32761,2,3,4,5,18 

Nature Communications (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Cognitive neuroscience
  • Computational neuroscience

Abstract

How functional brain networks and cognition co-evolve during adolescent development remains poorly understood. Using baseline and Year 2 data from 2949 individuals in the Adolescent Brain Cognitive Development Study, we trained kernel ridge regression models to predict cognitive ability from resting-state functional connectivity. We find that baseline functional connectivity more strongly predicts future cognitive ability than baseline cognitive ability. Models trained on baseline functional connectivity to predict baseline cognition generalize better to Year 2 functional connectivity and cognition, suggesting that brain–cognition relationships strengthen over time. Intriguingly, baseline functional connectivity outperforms longitudinal functional connectivity change in predicting future cognitive ability. While longitudinal functional connectivity change is less reliable than baseline functional connectivity – intraclass correlation coefficient 0.24 vs. 0.56 – shortening scan duration to reduce reliability of baseline functional connectivity does not eliminate the predictive gap. Furthermore, neither baseline functional connectivity nor functional connectivity change meaningfully predicts longitudinal change in cognitive ability. We also identify converging and diverging predictive network features across cross-sectional and longitudinal brain-cognition models – a multivariate twist on Simpson’s paradox – with clear sex-specific patterns. Overall, in early adolescence, stable individual differences in brain functional network organization play a more critical role than dynamic changes in shaping future cognitive outcomes.

Acknowledgements

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study® (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 and follow them over 10 years into early adulthood. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA 041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA04 1093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. ABCD data repository grows and changes over time. The ABCD data used in this report came from the NIMH Data Archive and were drawn from Release 4.0 (https://doi.org/10.15154/1523041) and Release 5.1 (https://doi.org/10.15154/z563-zd24).

Funding

B.T.T.Y. discloses support for the research of this work from the National University of Singapore Yong Loo Lin School of Medicine (NUHSRO/2020/124/TMR/LOA), the Singapore National Medical Research Council (NMRC) LCG (OFLCG19May-0035), NMRC CTG-IIT (CTGIIT23jan-0001), NMRC OF-IRG (OFIRG24jan-0006; OFIRG24j ul-0049), NMRC STaR (STaR20nov-0003), Singapore Ministry of Health (MOH) Centre Grant (CG21APR1009), and the United States National Institutes of Health (R01MH133334). All other authors declare no relevant funding. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders.

Author information

Authors and Affiliations

  1. Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Yapei Xie, Shaoshi Zhang, Csaba Orban, Leon Qi Rong Ooi, Ru Kong & B. T. Thomas Yeo

  2. Department of Medicine, Healthy Longevity Translational Research Programme, Human Potential Translational Research Programme & Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Yapei Xie, Shaoshi Zhang, Csaba Orban, Leon Qi Rong Ooi, Ru Kong & B. T. Thomas Yeo

  3. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Yapei Xie, Shaoshi Zhang, Csaba Orban, Leon Qi Rong Ooi, Ru Kong & B. T. Thomas Yeo

  4. N.1 Institute for Health, National University of Singapore, Singapore, Singapore

    Yapei Xie, Shaoshi Zhang, Csaba Orban, Leon Qi Rong Ooi, Ru Kong & B. T. Thomas Yeo

  5. Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore

    Shaoshi Zhang, Leon Qi Rong Ooi & B. T. Thomas Yeo

  6. Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland

    Dorothea L. Floris

  7. Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands

    Dorothea L. Floris

  8. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

    Xi-Nian Zuo

  9. National Basic Science Data Center, Beijing, China

    Xi-Nian Zuo

  10. Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Xi-Nian Zuo

  11. Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA

    Elvisha Dhamala

  12. Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA

    Elvisha Dhamala

  13. Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, NY, USA

    Elvisha Dhamala

  14. Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA

    Avram J. Holmes

  15. Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA

    Lucina Q. Uddin

  16. Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK

    Thomas E. Nichols

  17. Autism Center, Child Mind Institute, New York, NY, USA

    Adriana Di Martino

  18. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA

    B. T. Thomas Yeo

Authors
  1. Yapei Xie
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  2. Shaoshi Zhang
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  3. Csaba Orban
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  4. Leon Qi Rong Ooi
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  6. Dorothea L. Floris
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  7. Xi-Nian Zuo
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  8. Elvisha Dhamala
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  9. Avram J. Holmes
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  10. Lucina Q. Uddin
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  11. Thomas E. Nichols
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  12. Adriana Di Martino
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  13. B. T. Thomas Yeo
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Corresponding author

Correspondence to B. T. Thomas Yeo.

Ethics declarations

Competing interests

L.Q.R.O. and R.K. are co-founders of B1Neuro. B.T.T.Y. is a shareholder of B1Neuro. The content in this manuscript is unrelated to the activities of the company. The remaining authors declare no competing interests.

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Cite this article

Xie, Y., Zhang, S., Orban, C. et al. Convergent and divergent brain–cognition development in early adolescence. Nat Commun (2026). https://doi.org/10.1038/s41467-026-73668-y

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  • Received: 25 June 2025

  • Accepted: 12 May 2026

  • Published: 26 May 2026

  • DOI: https://doi.org/10.1038/s41467-026-73668-y

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