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  3. Refining Rdoc Using Individual-level Task Fmri Factor Models Reveals Reproducible And Clinically Relevant Brain-wide Motifs.
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  3. Refining Rdoc Using Individual-level Task Fmri Factor Models Reveals Reproducible And Clinically Relevant Brain-wide Motifs.

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Refining RDoC Using Individual-Level Task fMRI Factor Models Reveals Reproducible and Clinically Relevant Brain-Wide

Shaun K L Quah1, Saad Pirzada1, Soren Madsen1

  • 1Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.

Biorxiv : the Preprint Server for Biology
|June 4, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Data-driven models of brain organization outperform the Research Domain Criteria (RDoC) framework. Empirically derived models offer more accurate, generalizable, and behaviorally relevant insights for precision neuroscience.

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Computational Biology

Background:

  • The Research Domain Criteria (RDoC) framework aims to structure psychiatric research using biologically grounded, dimensional constructs.
  • The hierarchical structure of the RDoC framework lacks validation against individual-level brain and behavioral data.

Purpose of the Study:

  • To test whether individual-level, data-driven models more accurately capture brain activity and behavior organization than RDoC-based models.
  • To validate and refine dimensional psychiatric frameworks using empirical data.

Main Methods:

  • Applied a multi-stage validation framework to Human Connectome Project (HCP) task-fMRI data.
  • Utilized confirmatory factor analysis, community detection, and topological analysis on neural and behavioral data from HCP and LA5c datasets.
  • Included healthy controls and individuals with ADHD, bipolar disorder, and schizophrenia.

    • Main Results:

    • Data-driven bifactor models consistently outperformed RDoC-based models in capturing brain activity and behavior organization across independent cohorts.
    • Data-driven models revealed reproducible spatial motifs and a general factor aligning with cortical gradients, predicting task performance.
    • Data-driven models generalized across diagnostic groups and related to symptom severity, outperforming RDoC representations.

    Conclusions:

    • Individual-level, empirically derived models provide a more accurate, generalizable, and behaviorally relevant account of brain organization than the RDoC framework.
    • This work advances precision neuroscience by integrating neural, behavioral, and clinical validation.
    • Findings support the empirical refinement of dimensional psychiatric frameworks for improved mental health research and treatment.