A data-driven latent variable approach to validating the research domain criteria framework
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 em...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-55831-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832594559838715904 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | S. K. L. Quah |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9470eb820ba845fd8349df95ad2d5482 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9470eb820ba845fd8349df95ad2d54822025-01-19T12:32:05ZengNature PortfolioNature Communications2041-17232025-01-0116111110.1038/s41467-025-55831-zA data-driven latent variable approach to validating the research domain criteria frameworkS. K. L. Quah0B. Jo1C. Geniesse2L. Q. Uddin3J. A. Mumford4D. M. Barch5D. A. Fair6I. H. Gotlib7R. A. Poldrack8M. Saggar9Department of Psychiatry & Behavioral Sciences, School of Medicine, Stanford UniversityDepartment of Psychiatry & Behavioral Sciences, School of Medicine, Stanford UniversityMachine Learning & Analytics Group, Lawrence Berkeley National LaboratoryDepartment of Psychiatry and Biobehavioral Sciences, University of California Los AngelesDepartment of Psychology, Stanford UniversityDepartments of Psychological & Brain Sciences, Washington University in St. LouisMasonic Institute for the Developing Brain, University of MinnesotaDepartment of Psychology, Stanford UniversityDepartment of Psychology, Stanford UniversityDepartment of Psychiatry & Behavioral Sciences, School of Medicine, Stanford UniversityAbstract 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.https://doi.org/10.1038/s41467-025-55831-z |
| spellingShingle | 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 A data-driven latent variable approach to validating the research domain criteria framework Nature Communications |
| title | A data-driven latent variable approach to validating the research domain criteria framework |
| title_full | A data-driven latent variable approach to validating the research domain criteria framework |
| title_fullStr | A data-driven latent variable approach to validating the research domain criteria framework |
| title_full_unstemmed | A data-driven latent variable approach to validating the research domain criteria framework |
| title_short | A data-driven latent variable approach to validating the research domain criteria framework |
| title_sort | data driven latent variable approach to validating the research domain criteria framework |
| url | https://doi.org/10.1038/s41467-025-55831-z |
| work_keys_str_mv | AT sklquah adatadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT bjo adatadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT cgeniesse adatadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT lquddin adatadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT jamumford adatadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT dmbarch adatadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT dafair adatadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT ihgotlib adatadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT rapoldrack adatadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT msaggar adatadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT sklquah datadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT bjo datadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT cgeniesse datadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT lquddin datadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT jamumford datadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT dmbarch datadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT dafair datadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT ihgotlib datadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT rapoldrack datadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework AT msaggar datadrivenlatentvariableapproachtovalidatingtheresearchdomaincriteriaframework |