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From symptom-based heterogeneity to mechanism-based profiling in youth ADHD: the promise of computational psychiatry

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Abstract

Mental health conditions such as attention-deficit/hyperactivity disorder (ADHD) and mood disorders show marked symptom heterogeneity, complicating diagnosis and treatment. Computational psychiatry offers a way forward by using mathematical models, such as sequential sampling models, applied to trial-by-trial behavior in well-defined neurocognitive tasks, to infer latent mechanisms underlying behavior. In ADHD, this approach has revealed consistent alterations in information integration (reduced drift rates) in attention-demanding tasks and also indicates that combinations of different model parameters (increased drift rate and longer nondecision time) distinguish the different neurocomputational mechanisms that underlie symptom dimensions. Early work in ADHD also suggests that drift rate predicts illness trajectories and provides insights into treatment response. Yet current applications remain preliminary, limited by task constraints, assumptions in model specification, and questions of reliability and generalizability of the derived parameters. Integrating mechanistic modeling with naturalistic tasks, physiological measures, and longitudinal designs may help to disentangle context-specific from generalizable processes. Ultimately, shifting from symptom descriptions to mechanistic models of belief and behavioral adaptation in dynamic environments may pave the way for next-generation assessments in ADHD, and help to support interventions that are ecologically valid, developmentally informed, and adaptive to patients’ changing needs across time and context.

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Fig. 1: Conceptual framework and applications of sequential sampling models (SSMs) for characterizing latent decision-making processes.
Fig. 2: Potential link between childhood inattention and adult depressed mood, both characterized by longer encoding and execution times (SSM parameter: Ter) across two independent studies.

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Acknowledgements

The authors are deeply grateful to Sophia Vinogradov for her insightful editorial guidance, which greatly strengthened the clarity and organization of this review.

Funding

The present work was supported by the Swiss National Science Foundation Postdoc Award P500PS_214223 [to NGJ]. Dr. Pine’s work on this project was supported by NIMH-Intramural Research Program Project ZIA-MH002781. The funders had no role in study design, data collection, and analysis. This research was supported [in part] by the Intramural Research Program of the National Institutes of Health (NIH). The contributions of the NIH author(s) are considered Works of the United States Government. The findings and conclusions presented in this paper are those of the author(s) and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services.

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Authors and Affiliations

  1. Carney Institute for Brain Science, Department of Cognitive and Psychological Sciences, Brown University, Providence, RI, USA

    Nadja R. Ging-Jehli

  2. Center for Digital Health Interventions, School of Medicine, University of St. Gallen, ETH Zurich, & University of Zurich, Zurich/St.Gallen, Switzerland

    Nadja R. Ging-Jehli

  3. Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA

    Daniel S. Pine

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  1. Nadja R. Ging-Jehli
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  2. Daniel S. Pine
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NGJ conducted additional analyses, contributed to data interpretation, and drafted the initial manuscript. NGJ and DSP collaboratively developed the review’s conceptual framework, performed the literature synthesis, and co-wrote and revised the paper.

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Correspondence to Nadja R. Ging-Jehli.

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Ging-Jehli, N.R., Pine, D.S. From symptom-based heterogeneity to mechanism-based profiling in youth ADHD: the promise of computational psychiatry. Neuropsychopharmacol. (2025). https://doi.org/10.1038/s41386-025-02254-5

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