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Distinct brain network features predict internalizing and externalizing traits in children, adolescents and adults

Nature Mental Health volume 3, pages 306–317 (2025)Cite this article

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Abstract

The distinction between externalizing and internalizing traits has been a classic area of study in psychiatry. However, whether shared or unique brain network features predict internalizing and externalizing behaviors remains poorly understood. Using a sample of 5,260 children from the Adolescent Brain Cognitive Development study, 229 adolescents from the Healthy Brain Network and 423 adults from the Human Connectome Project, we show that predictive network features are, at least in part, distinct across internalizing and externalizing behaviors. Across all three samples, behaviors within internalizing and externalizing categories exhibited more similar predictive feature weights than behaviors between categories. These data suggest shared and unique brain network features account for individual variation within broad internalizing and externalizing categories across developmental stages.

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Fig. 1: Whole-brain FC predicts internalizing and externalizing behaviors in ABCD children.
Fig. 2: Distinct RSFC features predict internalizing and externalizing behaviors across all samples.
Fig. 3: Differential RSFC predictors of internalizing and externalizing behaviors across network blocks within each sample.
Fig. 4: Predictive RSFC feature weights associated with total internalizing problems and total externalizing problems in ABCD children, HBN adolescents and HCP adults.

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Data availability

The ABCD data are publicly available via the NIMH Data Archive (NDA) and via https://abcdstudy.org. The HBN data are publicly available via Child Mind Institute Healthy Brain Network at http://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network/Data.html. The HCP data are also publicly available and can be accessed via https://www.humanconnectome.org. Access to all three datasets requires Data Use Agreement.

Code availability

Code for this study is publicly available via Github under the main branch: https://github.com/quyueyue/InternalizingExternalizingPredictions.git. The software dependencies were Freesurfer (5.3.0; https://surfer.nmr.mgh.harvard.edu), FSL (5.0.8; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation), MATLAB (2018b; https://www.mathworks.com/products/matlab.html), Jupyter Notebook 6.4.5 (Python 3.9.7 ipykernel; https://jupyter.org), Python/3.10.8-GCCcore-12.2.0 (https://www.python.org) and the neuroCombat (v.1.0.13) package in R v.4.2.0.

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Acknowledgments

Data used in the preparation of this article were obtained, in part, 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 HCP adults. 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, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123 and U24DA041147. A full list of supporters is available at abcdstudy.org/federal-partners.html. A list of participating sites and a complete list of the study investigators can be found at 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. Additional data were provided, in part, by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH institutes and centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or the ABCD and HCP consortia investigators. This work was supported by the National Institute of Mental Health (R01MH120080 and R01MH123245 to A.J.H.). This work was also supported by the following awards to B.T.T.Y.: NUS 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 STaR (STaR20nov-0003), Singapore Ministry of Health (MOH) Centre Grant (CG21APR1009) and the Temasek Foundation (TF2223-IMH-01). This work was also supported by the following awards to E.D.: the Kavli Institute for Neuroscience at Yale University (Postdoctoral Fellowship for Academic Diversity), the Feinstein Institutes for Medical Research Advancing Women in Science and Medicine (Career Development Award and Barbara Zucker Emerging Scientist Award). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the funders. An earlier version of this paper is available at BioRxiv (https://doi.org/10.1101/2023.05.20.541490v1).

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Authors and Affiliations

  1. Department of Psychology, Yale University, New Haven, CT, USA

    Yueyue Lydia Qu, Carrisa V. Cocuzza & Connor Lawhead

  2. Wu Tsai Institute, Yale University, New Haven, CT, USA

    Yueyue Lydia Qu & Carrisa V. Cocuzza

  3. Center for Sleep and Cognition, National University of Singapore, Singapore, Singapore

    Jianzhong Chen, Angela Tam, Leon Qi Rong Ooi, Shaoshi Zhang, Tianchu Zeng & B. T. Thomas Yeo

  4. Center for Translational MR Research, National University of Singapore, Singapore, Singapore

    Jianzhong Chen, Angela Tam, Leon Qi Rong Ooi, Shaoshi Zhang, Tianchu Zeng & B. T. Thomas Yeo

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

    Jianzhong Chen, Angela Tam, Leon Qi Rong Ooi, Shaoshi Zhang, Tianchu Zeng & B. T. Thomas Yeo

  6. N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore

    Jianzhong Chen, Angela Tam, Leon Qi Rong Ooi, Shaoshi Zhang, Tianchu Zeng & B. T. Thomas Yeo

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

    Leon Qi Rong Ooi & B. T. Thomas Yeo

  8. Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, NY, USA

    Elvisha Dhamala

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

    B. T. Thomas Yeo

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

    Avram J. Holmes

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Contributions

Y.L.Q. and A.J.H. designed the research, analyzed and interpreted the results, made figures and wrote the paper. Y.L.Q. conducted the research, analyzed the data and reviewed and published the code. Y.L.Q., J.C., A.T., L.Q.R.O., E.D., C.V.C., S.Z., T.Z., C.L., B.T.T.Y. and A.J.H. provided analytic support. Y.L.Q., J.C., A.T., L.Q.R.O., E.D., C.V.C., S.Z., T.Z., C.L., B.T.T.Y. and A.J.H. edited the paper.

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Correspondence to Yueyue Lydia Qu or Avram J. Holmes.

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Qu, Y.L., Chen, J., Tam, A. et al. Distinct brain network features predict internalizing and externalizing traits in children, adolescents and adults. Nat. Mental Health 3, 306–317 (2025). https://doi.org/10.1038/s44220-025-00388-5

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