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Mapping the neurodevelopmental predictors of psychopathology

Molecular Psychiatry (2024)Cite this article

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Abstract

Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is an essential step towards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r2 < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.

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Fig. 1: Multimodal models were the most accurate at predicting generalized psychopathology.
Fig. 2: NODDI microstructural properties and cortical thickness were the most heavily weighted among the multimodal models.
Fig. 3: The predictive power gained from cortical thickness and ODI was widely distributed throughout the brain.
Fig. 4: Features extracted exclusively from the default mode network derived the most accurate predictions of the general psychopathology factor.
Fig. 5: Cortical thickness of the default mode network provided the largest contribution to the predictive power of our cortex-wide models.
Fig. 6: Differences in the most important neural markers of internalizing, externalizing, and the general psychopathology.

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Data availability

The Lifespan Human Connectome Project in Development is a publicly available dataset that can be accessed from the National Data Archive repository.

Code availability

All code pertaining to this study has been made publicly available, and our computational modeling scripts were written in either R version 4.3.1 or Python version 3.10.4: https://doi.org/10.5281/zenodo.8378010.

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Acknowledgements

This work was supported by the National Science Foundation Graduate Research Fellowship (DGE-2139839; DGE-1745038), the National Research Service Award from the National Institute of Mental Health (F31MH135640). Additional support was provided by the National Institute of Mental Health grant R01MH129493. Computations were performed using the facilities of the Washington University Research Computing and Informatics Facility (RCIF), which received funding from two National Institutes of Health S10 program grants: 1S10OD025200-01A1 and 1S10OD030477-01. Additionally, we would like to thank Petra Lenzini and Mark Curtis for their assistance with preprocessing and aggregating the data for this project.

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Authors and Affiliations

  1. Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA

    Robert J. Jirsaraie

  2. Lifespan Brain Institute, Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA

    Martins M. Gatavins

  3. Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA

    Adam R. Pines

  4. Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA

    Sridhar Kandala, Ryan Bogdan & Deanna M. Barch

  5. Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA

    Janine D. Bijsterbosch, Scott Marek, Deanna M. Barch & Aristeidis Sotiras

  6. Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA

    Scott Marek

  7. AI for Health Institute, Washington University School of Medicine in St. Louis, St. Louis, MO, USA

    Scott Marek

  8. Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA

    Deanna M. Barch

  9. Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA

    Aristeidis Sotiras

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  3. Adam R. Pines
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  4. Sridhar Kandala
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  9. Aristeidis Sotiras
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Contributions

RJJ contributed to all parts of this project from conception to drafting the manuscript. MMG contributed to writing the code for implementing tree-based algorithms. ARP and SK assisted with preprocessing and interpreting multimodal MRI data. The conception and design of this project was facilities by JDB, SM, and RB. The conceptualization, design, interpretation of results, and manuscript writing were facilitated by DMB and AS.

Corresponding author

Correspondence to Aristeidis Sotiras.

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Competing interests

AS is a shareholder in TheraPanace and has received compensation for serving as a grant reviewer for the BrightFocus Foundation. All other authors declare no biomedical financial interests or potential conflicts of interest.

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Jirsaraie, R.J., Gatavins, M.M., Pines, A.R. et al. Mapping the neurodevelopmental predictors of psychopathology. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-024-02682-7

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