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Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort

Nature Computational Science volume 5, pages 221–233 (2025)Cite this article

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A preprint version of the article is available at Research Square.

Abstract

Despite the mounting demand for generative population models, their limited generalizability to underrepresented demographic groups hinders widespread adoption in real-world applications. Here we propose a diversity-aware population modeling framework that can guide targeted strategies in public health and education, by estimating subgroup-level effects and stratifying predictions to capture sociodemographic variability. We leverage Bayesian multilevel regression and post-stratification to systematically quantify inter-individual differences in the relationship between socioeconomic status and cognitive development. Post-stratification enhanced the interpretability of model predictions across underrepresented groups by incorporating US Census data to gain additional insights into smaller subgroups in the Adolescent Brain Cognitive Development Study. This ensured that predictions were not skewed by overly heterogeneous or homogeneous representations. Our analyses underscore the importance of combining Bayesian multilevel modeling with post-stratification to validate reliability and provide a more holistic explanation of sociodemographic disparities in our diversity-aware population modeling framework.

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Fig. 1: Diversity-aware population modeling schematic.
Fig. 2: Uncertainty estimates for VIVS models.
Fig. 3: Post-stratification adjustments across state and race subgroups.
Fig. 4: Post-stratification adjustments across state, race and sociodemographic subgroups.
Fig. 5: Geospatial visualization of post-stratification corrections to overall cognition predictions.
Fig. 6: Joint distribution of varying slopes showing overlapping high-confidence effects.

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Data availability

The data supporting the findings of this study are available from the ABCD and Robert Graham Center’s SDI dataset15. The ABCD dataset is a publicly available resource accessible through the National Institute of Mental Health Data Archive. All relevant instructions to obtain the data can be found online at: https://nda.nih.gov/abcd/request-access. Source Data are provided with this paper.

Code availability

The processing scripts and custom analysis software used in this work are publicly available via GitHub52 and via figshare53.

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Acknowledgements

D.B. was supported by the Brain Canada Foundation, through the Canada Brain Research Fund, with the financial support of Health Canada, National Institutes of Health (NIH R01 AG068563A, NIH R01 DA053301-01A1, NIH R01 MH129858-01A1), the Canadian Institute of Health Research (CIHR 438531, CIHR 470425), the Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund), the IVADO R3AI initiative (Canada First Research Excellence fund), and by the CIFAR Artificial Intelligence Chairs program (Canada Institute for Advanced Research).

Author information

Authors and Affiliations

  1. McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal, Quebec, Canada

    Nicole Osayande, Justin Marotta, Shambhavi Aggarwal, Jakub Kopal & Danilo Bzdok

  2. Mila—Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada

    Nicole Osayande, Justin Marotta, Shambhavi Aggarwal, Jakub Kopal & Danilo Bzdok

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

    Avram Holmes

  4. Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

    Sarah W. Yip

  5. Child Study Center, Yale University School of Medicine, New Haven, CT, USA

    Sarah W. Yip

  6. Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada

    Danilo Bzdok

  7. School of Computer Science, McGill University, Montreal, Quebec, Canada

    Danilo Bzdok

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Contributions

D.B. and N.O. designed the study, analyzed sociodemographic and behavioral data, and drafted the manuscript. J.M., S.A., J.K., A.H. and S.W.Y. provided feedback on the manuscript.

Corresponding authors

Correspondence to Nicole Osayande or Danilo Bzdok.

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Competing interests

D.B. is a shareholder and advisory board member at MindState Design Labs, USA. The other authors declare no competing interests.

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Nature Computational Science thanks Joyonna Gamble-George, Arianna Gard and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Fernando Chirigati, in collaboration with the Nature Computational Science team.

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Extended data

Extended Data Fig. 1 A comparison of the participant distribution in the ABCD cohort and in the US Census.

A, To examine potential sampling discrepancies between the ABCD cohort and the US Census SDI, participant distributions are compared across race and state strata. This shows that Asian participants in Missouri are represented only in the SDI and not in the ABCD cohort. B, Pearson’s correlation coefficients, |ρ|, are visualized to identify which SDV–neurobehavior relationships to model. Based on our set threshold of |ρ| ≥ 0.2, we identified 14 pairs (out of 2052 candidate pairs) with the strongest positive and negative associations, narrowing our study to 5 SDVs and 3 cognitive phenotypes.

Source data

Supplementary information

Source data

Source Data Fig. 2

94% HDI interval for each intercept and slope estimation.

Source Data Fig. 3

Mean predictions before and after post-stratification.

Source Data Fig. 4

Mean predictions before and after post-stratification for each binary SES group.

Source Data Fig. 5

Mean predictions before and after post-stratification for each state.

Source Data Extended Data Fig. 1

Race–state distribution counts and Pearson’s cross-correlation coefficients.

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Osayande, N., Marotta, J., Aggarwal, S. et al. Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort. Nat Comput Sci 5, 221–233 (2025). https://doi.org/10.1038/s43588-025-00774-0

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