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Secondary Data Analyses to Explore RDoC Domains


Sarah Morris, Ph.D.
Chief, Schizophrenia Spectrum Disorders Research Program
Division of Adult Translational Research and Treatment Development


This initiative aims to support secondary analyses of existing datasets, by leveraging clinical research data to investigate specific domains within the Research Domain Criteria (RDoC) project or test novel hypotheses that are consistent with RDoC.


To implement NIMH’s RDoC initiative, the Institute is currently funding a series of new research projects via the RDoC-targeted Requests for Applications and investigator-initiated applications. It is likely, however, that many already existing datasets could be re-analyzed within the RDoC framework. The proposed initiative would support projects that would exploit existing data to explore novel ways of grouping participants in clinical studies.

Research applications under this initiative could involve the re-organization of data from patients and comparison participants according to the dimensional RDoC constructs, merging of similar datasets from different patient groups to allow cross-diagnostic comparisons, and/or incorporation of data from participants who do not meet DSM/ICD criteria for categorical diagnoses. Datasets that include a range of participant ages could be combined or re-aligned to examine developmental factors related to the RDoC constructs. Rather than focusing on any specific RDoC domain(s), investigators would be encouraged to propose the analyses for which they have the most appropriate data. Databases that include measures that align with RDoC constructs and multiple units of analysis (genes, molecules, neural circuits, physiology, behavior, self-report) would be optimal.

This initiative would accomplish the following goals:

  1. Encourage and allow investigators to test hypotheses about RDoC constructs without the cost and effort required to initiate an entirely new project;
  2. Promote the development of new collaborative relationships for the purposes of combining datasets;
  3. Expedite the work of validating the RDoC constructs; and,
  4. Stimulate new hypotheses and pilot data for future RDoC projects.

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