Evidence map›Paper›PMID 39827137›Full record

ArticleNature communications2025

A data-driven latent variable approach to validating the research domain criteria framework.

S K L Quah, B Jo, C Geniesse, L Q Uddin, J A Mumford, D M Barch, D A Fair, I H Gotlib, R A Poldrack, M Saggar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Psychiatry in the real world.Frontiers in psychiatry · 2025
    Article
  6. Article
  7. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

S K L QuahDepartment of Psychiatry & Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA. shaunq@stanford.edu.ORCID http://orcid.org/0000-0002-8857-1320
B JoDepartment of Psychiatry & Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA.
C GeniesseMachine Learning & Analytics Group, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
L Q UddinDepartment of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA.
J A MumfordDepartment of Psychology, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-0926-3531
D M BarchDepartments of Psychological & Brain Sciences, Washington University in St. Louis, St Louis, MO, USA.ORCID http://orcid.org/0000-0003-1693-8506
D A FairMasonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.ORCID http://orcid.org/0000-0001-8602-393X
I H GotlibDepartment of Psychology, Stanford University, Stanford, CA, USA.
R A PoldrackDepartment of Psychology, Stanford University, Stanford, CA, USA.
M SaggarDepartment of Psychiatry & Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA. saggar@stanford.edu.ORCID http://orcid.org/0000-0002-5307-2249

Funding

17/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UCLAU01DA050987 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SUSAN Y BOOKHEIMER, Mirella Dapretto · 2020 to 2026
$9.0M
Psychobiological Mechanisms Underlying the Association Between Early Life Stress and Depression Across AdolescenceR37MH101495 · NIMH · STANFORD UNIVERSITY · PI IAN H GOTLIB · 2018 to 2026
$6.6M
Examining the hierarchical structure of the RDoC framework using large-scale data-driven computational approachesR01MH127608 · NIMH · STANFORD UNIVERSITY · PI SAGGAR, MANISH · 2021 to 2025
$3.3M
Exploratory investigation of bilingualism, executive function, and brain organization in children with autismR21HD111805 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI UDDIN, LUCINA QAZI · 2023 to 2023
$432k
NICHD NIH HHS R21 HD111805NIDA NIH HHS U01 DA050987NIMH NIH HHS R01 MH127608NIMH NIH HHS R37 MH101495U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH127608
6 · The paper itself

Abstract

Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns, we employ a latent variable approach using bifactor analysis. We examine 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with 6192 participants. A curated subset of 37 maps with a balanced representation of RDoC domains constitute the training set, and the remaining held-out maps form the internal validation set. External validation is conducted using 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Here, we show that a bifactor model incorporating a task-general domain and splitting the cognitive systems domain better fits the examined corpus of tfMRI data than the current RDoC framework. We also identify the domain of arousal and regulatory systems as underrepresented. Our data-driven validation supports revising the RDoC framework to reflect underlying brain circuitry more accurately.

Indexed as

BrainBrain MappingAdultFemaleHumansMagnetic Resonance ImagingMale

Identifiers

PMID39827137
PMCPMC11743195

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.