# Examining the hierarchical structure of the RDoC framework using large-scale data-driven computational approaches

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $678,411

## Abstract

Project Summary
The Research Domain Criteria (RDoC) applies an integrative, dimensional approach anchored in circuit
neuroscience, genes, molecules, and behaviors. The RDoC framework, currently only for research, ultimately
aims at facilitating the development of psychiatric nosology (disorder-classification system) based upon
primary behavioral functions and their associated biological features that the brain has evolved to carry out.
Although the impetus behind RDoC is in the right direction, for greater efficacy of RDoC in clinical translation, a
data-driven examination is needed to validate and refine the architecture of RDoC. Further, several key
questions remain unanswered. First, as noted in the current RFA (RFA-MH-19-242), since the inception of
RDoC, a thorough data-driven validation that broadly explores, compares, and validates the constructs within
the framework has not been performed. Second, to increase clinical translation of the RDoC framework, it is
essential to assess whether constructs within a domain consistently relate to similar dimensions of
psychopathology. Thus, providing data-driven evidence for the convergent and discriminant validity of the
RDoC framework in predicting psychopathology. Lastly, and perhaps more fundamentally, it is unclear whether
carefully crafted behavioral paradigms are required to examine domain-specific features (behavioral or circuit-
level) or task-free paradigms (e.g., resting-state) can be computationally employed to extract similar domain-
specific features. The lack of task instructions in resting-state paradigms enhances compliance in clinical
populations, makes data aggregation across sites straightforward, and could provide a higher cost-benefit ratio
if a single resting-state scan can provide information that would otherwise require multiple, carefully crafted,
domain-specific neuroimaging task scans. Here, we propose to mine, systemically and computationally, three
large-scale datasets from the general population and diagnosed patient populations to answer critical
questions regarding the validity of the RDoC framework. Specifically, we aim to examine whether: (1) within-
domain constructs overlap more than do between-domain constructs; (2) within-domain constructs relate to
similar dimensions of psychopathology; and (3) task-free paradigms (e.g., resting-state) can be mined to
extract similar domain-specific information that is usually extracted using specific task-based paradigms. By
addressing these three key questions, our central goal is to provide the much-needed bottom-up examination
of the RDoC framework to pave a pathway for its refinement and translation. Our long-term goal is to develop
new computational frameworks to generate converging insights for grounding psychiatric nosology in biological
features. Altogether, without careful data-driven validation, the RDoC framework remains theoretical. Hence,
we advocate for developing a computational backbone for the RDoC framework t...

## Key facts

- **NIH application ID:** 10455569
- **Project number:** 5R01MH127608-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Manish Saggar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $678,411
- **Award type:** 5
- **Project period:** 2021-07-22 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10455569

## Citation

> US National Institutes of Health, RePORTER application 10455569, Examining the hierarchical structure of the RDoC framework using large-scale data-driven computational approaches (5R01MH127608-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10455569. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*