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RDoC at Ten Years: Part 1


Ten years ago, in July, more than 20 extramural scientists — psychologists, psychiatrists, cognitive scientists, neuroscientists, and others — gathered in Bethesda, Maryland. Joined there by a couple of dozen NIMH staff, they spent three days discussing what we knew and what we didn’t know about a particular domain of cognition: working memory. But this meeting wasn’t your typical academic consideration of a topic. It was the first of a series of workshops that set in motion an effort to transform how we investigate mental illnesses. With these workshops and an accompanying commentary , also published in July of 2010, the Research Domain Criteria (RDoC) initiative was created.

RDoC was born of the recognition that the established practice of studying people with disorders defined by traditional diagnostic categories as listed in the Diagnostic and Statistical Manual (DSM), a standardized diagnostic system used in psychiatry, had not significantly advanced our understanding of mental illnesses for some time. Results from studies employing genetics, functional brain imaging, clinical psychopharmacology, and sophisticated behavioral measurements raised doubts about the validity of diagnoses based on symptoms, such as those in the DSM. More and more, scientists and clinicians alike began to appreciate the blurred boundaries between categorical disorders, and the heterogeneity within them. Furthermore, results of studies aimed at examining the neural underpinnings of disorders defined by traditional diagnostic criteria frequently failed to replicate. Taken together, these findings led to the recognition that traditional diagnostic systems were not capturing the true underlying structures of mental illnesses.

The RDoC idea was relatively straightforward. First, that studying psychopathology in its various realms of function (such as working memory or reward-related activity), as opposed to organized by traditional disorder categories, might hew more closely to the underlying biology. And second, that describing an individual’s level of function within a domain as a dimensional characteristic (how well a person performs in a given domain, from typical to varying degrees of abnormality) would yield a better characterization of the individual’s functional strengths and weaknesses than a categorical description.

To assist investigators in developing and testing dimensional, domain-level hypotheses, NIMH created a systematized “first draft” set of exemplar domains of psychological function, including cognition, social processing, positive and negative valence, and more, and encouraged scientists to submit applications characterizing transdiagnostic samples of participants according to their performance in these domains.

In effect, the creation of RDoC was in itself a test of a hypothesis, as I suggested in a previous message: that studying psychopathology characterized by dimensional performance in a set of functional domains would facilitate the discovery of underlying neural mechanisms and opportunities for developing new therapies. It is worthwhile to ask, then, how this hypothesis has fared over the first ten years of RDoC. 

The RDoC transdiagnostic, dimensional approach has been reflected in the methodology of a number of large sample studies that aggregate data from patients with different disorders but common functional deficits. For example, the Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP) study used a sample of thousands of people with psychosis in the context of a variety of diagnoses. The researchers found that characterizing these individuals functionally, with a variety of behavioral and physiological measures, led to the identification of subgroups of individuals that were more consistent and less heterogeneous  than when the same individuals were grouped together by diagnoses. Similarly, transdiagnostic studies of emotional or cognitive control find aspects of dysfunction that are similar across individuals with different DSM diagnoses; groups of individuals formed by these functional measures seem to have more in common from a neurobiological perspective than when grouped by DSM diagnosis. Together, these and other studies argue that dimensional, domain-driven approaches can help group like with like and identify underlying neurobiology with greater fidelity.

Thanks to findings such as these, the RDoC framework has changed the conversation in mental health research. It is now common to acknowledge that traditional diagnoses are broad syndromes marked by heterogeneity, co-morbidity, and imprecision, and therefore that precision-medicine approaches are needed. This shift is reflected in new open-mindedness to novel recruitment and classification approaches in peer review of grant applications. Hundreds of grants and thousands of papers reference the RDoC framework. It has even resulted in a change in title for a major psychiatric research journal ! Finally, the RDoC framework has captured the attention of animal researchers and drug and device companies, recognizing that the greater reliability and better measurement supported by the dimensional, domain-based approach may facilitate studies of neural circuitry, strategies for target identification, and studies of target engagement and clinical efficacy .

It is important to note that RDoC is not a diagnostic system, nor is it meant to be. It is a research framework intended to change as new data are gathered and new concepts are realized. In its first ten years, the RDoC framework has encouraged novel approaches and new directions of research that continue to generate exciting and important findings. In the next ten years, we’ve got even bigger plans as we shift towards increased use of theory-driven behavioral models and quantitative analyses of large datasets. More on that in the next Director’s Message. In the meantime, Happy 10th Birthday, RDoC!


Beam, E., Potts, C., Poldrack, R. A., & Etkin, A. A computational knowledge engine for human neuroscience. bioRxiv 701540; doi:

Carpenter, W. T. (2016). Shifting paradigms and the term schizophrenia. Schizophrenia Bulletin, 42(4), 863-864. doi: 10.1093/schbul/sbw050

Clementz, B. A., Sweeney, J. A., Hamm, J. P., Ivleva, E. I., Ethridge, L. E., Pearlson, G. D., Keshavan, M. S., & Tamminga, C. A. (2016). Identification of distinct psychosis biotypes using brain-based biomarkers [published correction appears in Am J Psychiatry]. American Journal of Psychiatry, 173(4), 373-384. doi: 10.1176/appi.ajp.2015.14091200

Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010). Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. The American Journal of Psychiatry, 167(7), 748-751. doi:  10.1176/appi.ajp.2010.09091379

Krystal, A. D., Pizzagalli, D. A., Smoski, M., Mathew, S. J., Nurnberger, J., Lisanby, S. H., Iosifescu, D., Murrough, J. W., Yang, H., Weinger, R. D., Calabrese, J. R., Sanacora, G., Hermes, G., Keefe, R. S. E., Song, A., Goodman, W., Szabo, S. T., Whitton, A. E., Gao, K., & Potter, W. Z. (2020). A randomized proof-of-mechanism trial applying the ‘fast-fail’ approach to evaluating kappa-opioid antagonism as a treatment for anhedonia. Nature Medicine. doi:10.1038/s41591-020-0806-7

McTeague L. M., Huemer, J., Carreon, D. M., Jiang, Y., Eickhoff, S. B., & Etkin A. (2017). Identification of common neural circuit disruptions in cognitive control across psychiatric disorders. The American Journal of Psychiatry, 174(7), 676-685. doi: 10.1176/appi.ajp.2017.16040400

McTeague, L. M., Rosenberg, B. M., Lopez, J. W., Carreon, D. M., Huemer, J., Jiang, Y., Chick, C. F., Eickhoff, S. B., & Etkin, A. (2020). Identification of common neural circuit disruptions in emotional processing across psychiatric disorders. The American Journal of Psychiatry. doi: 10.1176/appi.ajp.2019.18111271