Abstract
The National Institute of Mental Health (NIMH) addressed in its 2008 Strategic Plan an emerging concern that the current diagnostic system was hampering translational research, as accumulating data suggested that the system’s disorder categories constituted heterogeneous syndromes rather than specific diseases. However, established practices in peer review placed high priority on that system’s disorders in evaluating grant applications for mental illness. To provide guidelines for alternative study designs, NIMH set a goal to develop new ways of studying psychopathology based on dimensions of measurable behavior and related neurobiological measures. The Research Domain Criteria (RDoC) project is the result, intended to build a literature that informs new conceptions of mental illness and future revisions to diagnostic manuals. The framework calls for the study of empirically derived fundamental dimensions characterized by related behavioral/psychological and neurobiological data (e.g., reward valuation, working memory). RDoC also emphasizes approaches including neurodevelopment, environmental effects, and the full range of dimensions of interest (from typical to increasingly abnormal), as well as research designs that integrate data across behavioral, biological, and self-report measures. This article provides an overview of the project’s first decade and its potential future directions. RDoC remains grounded in experimental psychopathology perspectives, and its progress is strongly linked to psychological measurement and integrative approaches to brain-behavior relationships.
Keywords
The Research Domain Criteria (RDoC) project is a framework for research on mental disorders that focuses on dimensions of behavioral and psychological functioning and their implementing neural circuits. RDoC originated from one element of the 2008 National Institute of Mental Health (NIMH) Strategic Plan for Research, a document motivated throughout by the need to accelerate progress in reducing the burden of suffering from mental illness.
Unveiled in 2010, RDoC addresses an emergent obstacle to progress regarding the institute’s mission: the use of traditional diagnostic manuals for research on mental disorders. The primary manual in the United States is the Diagnostic and Statistical Manual of Mental Disorders, currently in its fifth edition (DSM-V; American Psychiatric Association, 2013). Its fundamental architecture remains based upon the third edition (DSM-III; American Psychiatric Association, 1980), in which disorder categories were defined primarily by sets of signs and symptoms. This approach was developed to optimize reliability of diagnosis, but the validity of disorder classes has been questioned as a result of indeterminate and inconsistent findings from contemporary scientific fields, such as neuroimaging, sophisticated behavioral science, and genetics (Kapur et al., 2012). As summarized in the 2008 NIMH Strategic Plan, the way that mental disorders are defined in the present diagnostic system does not incorporate current information from integrative neuroscience research, and thus is not optimal for making scientific gains. . . . It is difficult to deconstruct clusters of complex behaviors and attempt to link these to underlying biological systems. (p. 9)
The problem was not so much the diagnostic system per se, but rather that disorder categories became reified soon after the release of DSM-III and became the norm for peer-review committees in evaluating grant applications about mental disorders. A former NIMH director summarized the issue this way: The DSM system was a critical platform for research that made possible shared understandings of disease models or affected populations under study. At the same time, it created an unintended epistemic prison that was palpably impeding scientific progress. Outside of their ongoing research projects, most investigators understood that the DSM-IV was a heuristic, pending the advance of science. In practice, however, [for grant applications] DSM-IV diagnoses controlled the research questions they could ask, and perhaps, even imagine. (Hyman, 2010, p. 157)
Scientific review depends on conceptual paradigms shared among applicants and reviewers, and the DSM’s hegemony was mostly due to a lack of alternative approaches. RDoC thus propounds directions that diverge from traditional study designs in which a single DSM patient group (e.g., patients with bipolar disorder) is compared with a healthy control group; instead, in the tradition of experimental psychopathology, RDoC provides a new set of guidelines and criteria for studying the ways that basic functions (e.g., cognitive control, reward processing) become dysregulated and eventuate in symptoms and impairment.
The framework has been extensively described elsewhere (Cuthbert & Kozak, 2013; Kozak & Cuthbert, 2016; NIMH, n.d.) and is only summarized briefly here (see Fig. 1). Constructs similar to usual psychological constructs are the main focus of experimental attention but are defined in terms of empirical evidence for both a basic functional dimension of behavior or psychological processes and a neural circuit or system implementing the function. Constructs are nested in six broad domains (as shown in Fig. 1); for instance, the Cognitive Systems domain includes such constructs as attention, cognitive control, and perception.
Major elements of the Research Domain Criteria (RDoC) framework. Each of the six domains contains three to six related constructs (and additional subconstructs in some instances), which are the main focus of study. The vertical blue bars depict examples of various units of analysis that might be incorporated in RDoC studies; circuits, the unit of convergence, are composed of neural systems and implement behavioral dimensions. The neurodevelopment arrow represents a life-span approach, starting with conception and continuing through the stages of adulthood; environment is used as a catchall term for all potential aspects of the environment that might be included in study designs, such as family, schools, neighborhoods, and culture, but also individual events, such as accidents or assaults. Inclusion of environmental variables in study designs is encouraged. Reprinted from About RDoC, by National Institute of Mental Health, n.d. (https://www.nimh.nih.gov/research/research-funded-by-nimh/rdoc/about-rdoc). In the public domain.
Experimental designs typically focus on one or two constructs, so studies are narrower in scope than typical DSM research but much more tightly connected to mechanisms across multiple systems. Constructs are regarded from a dimensional perspective that covers the full range from normality to varying degrees of dysfunction so that transitions from healthy to increasingly abnormal performance can be explicated. RDoC prioritizes research that includes a substantial proportion of treatment-seeking participants (and control participants with a range of psychopathology that does not reach the level of a diagnosis); experiments comprising only normal-range participants are typically not considered as RDoC projects (although they are potentially appropriate for basic-science grant applications).
Investigators are encouraged to acquire data (termed “units of analysis,” e.g., circuit measures, behavioral performance, self-reports) from multiple response systems in order to address the well-known lack of coherence among measurement classes. High priority is placed on neurodevelopmental studies and environmental effects. An essential point is that the domains, constructs, and units of analysis are considered to be heuristic exemplars rather than fixed sets. Fostering development of new or refined domains, constructs, and measurement techniques is salient; it is the principles of the framework, rather than current specific elements, that constitute the project’s capstone.
Progress
The RDoC project has attracted considerable attention in all endeavors related to mental disorders, including research, clinical practice, and philosophical debates about the nature of mental illness. Given the complex and extensive nature of RDoC activities, this brief review highlights a few selected topics with particular relevance to psychological science.
Dissemination and discussion
RDoC has generated a sizable body of experimentation and commentary across multiple areas of science (Gordon, 2020a). The National Institutes of Health RePORTER grants database lists nearly 500 active grants referring to RDoC, funded mostly by NIMH but by other institutes as well. In June 2021, a Google search on “NIMH ‘Research Domain Criteria’” returned more than 150,000 hits, comprising a broad mix of scientific reports, theoretical commentaries, clinical applications, and various blogs that reflect a full range of positive to negative opinions. Notably, the project has attracted wide attention internationally (e.g., Schumann et al., 2014).
Views of mental disorders have changed considerably over the past decade, and RDoC likely has played a significant role in that shift. (Related initiatives include the move from diseases to syndromes in DSM-V and other efforts to develop alternative conceptual and research approaches to psychopathology—e.g., Borsboom, 2017; Kotov et al., 2017.) For example, an editorial in a major schizophrenia journal noted that an emerging change in research priorities reflects a new emphasis on porous diagnostic boundaries with increased attention to similarities and differences between disorders. Also, a focus on deconstructing heterogeneous clinical syndromes in order to identify specific elements of pathology is advancing science, often in a dimensional framework without diagnostic specificity. (Carpenter, 2016, p. 863)
This statement is consistent with a marked increase in occurrences of the term “transdiagnostic” in the literature since RDoC began; a PubMed search tabulating four successive 3-year epochs from 2009 to 2020 returned 92, 308, 953, and 1,745 hits, respectively. 1
Clinical applications
These changes have begun to spread from academic research to clinical discussions. For instance, as stated in a recent psychiatric trade publication, Over the last decade or so, our field has experienced a rapid shift in our understanding of schizophrenia and other serious psychotic disorders. . . . Accumulating evidence indicates that psychotic disorders constitute syndromes rather than diseases per se. . . . Patients with different clinical diagnostic phenotypes (such as schizophrenia rather than bipolar disorder with psychosis) can show similar underlying patterns of cognitive dysfunction and neurobiological abnormalities. (Vinogradov, 2019, pp. 4–5)
Further, clinical assessments and treatment inspired by RDoC are beginning to appear (e.g., Shinn et al., 2017). Although many factors (e.g., insurance reimbursements tied to traditional diagnoses) slow progress in clinical innovation, consideration of more precise assessments and treatments is underway.
RDoC principles have also extended into clinical treatment studies, arriving sooner than expected because pharmaceutical companies have turned away from developing drugs for mental disorders as a result of the familiar problems of heterogeneity and comorbidity. Constructs intended to link psychological aspects of disorders (anhedonia, cognitive impairment) with biological systems offer potential new, precision-medicine targets for compounds and medical devices; for example, a recent proof-of-concept trial demonstrated the use of a kappa-opioid blocker as a potential treatment for anhedonia (Krystal et al., 2020). The same treatment principles apply for behavioral treatments as well (Premo et al., 2020). Such developments reinforce the need for new measurement tools, both to provide valid constructs for treatment targets and to supply instruments for initial assessment and measurement of clinical outcomes.
Reductionism and mind-body issues
Early work that included statements like “mental disorders are brain disorders” understandably prompted the impression that RDoC advocated a purely biological, reductionistic view of mental illness (Insel, 2009). However, such statements were better interpreted as attempts to shift the zeitgeist from phenomenological views of disorder to a more balanced, multisystems approach. As an early RDoC commentary noted, “an essential point is that the RDoC initiative does not rely upon assumptions of eliminative reductionism, or even of biological fundamentalism” (Cuthbert & Kozak, 2013, p. 931). Rather, a major goal of RDoC is to address mind-body issues directly by focusing on dimensions that are understood by conjoining psychological and biological aspects (Kozak & Cuthbert, 2016). RDoC builds upon theories that acknowledge the need to address empirically the typically modest covariation observed among various response systems (e.g., the three-systems model of emotion; Lang, 2010), with an emphasis on mechanistic relationships that do not privilege particular measurement classes (Lake et al., 2017).
Developmental research
Development studies are a high priority for RDoC. Almost all mental disorders have neurodevelopmental origins, manifesting genetic influences, growth trajectories, and multiple domains of environmental influences that interact with development (Pollak, 2015). RDoC principles comport particularly well with developmental disorders, which are now generally understood to involve multiple dimensions that overlap considerably across diagnostic categories. Their extensive heterogeneity and comorbidity have resulted in increasing calls to move beyond current nosologies to explore dimensional and transdiagnostic mechanisms (e.g., Nigg, 2015), transcending “core-deficit” hypotheses of specific disorders (Astle & Fletcher-Watson, 2020). A high priority is to focus on ways of depicting RDoC constructs across development, given evolving trajectories of psychological and behavioral functions and brain circuits. In this regard, commentaries have provided thoughtful contributions as to how developmental studies can be considered from the RDoC perspective (e.g., Mittal & Wakschlag, 2017) and how misguided assumptions in the current literature can be addressed in moving toward future research agendas (Beauchaine & Hinshaw, 2020).
One somewhat underdeveloped aspect of developmental work under the RDoC aegis relates to the normal-to-abnormal dimensions of constructs. The study of dimensional functions as they progress across neurodevelopment, and interact with environmental influences, may provide information about changes in risk or resilience that could provide opportunities for prevention that are not feasible from the perspective of binary (well/sick), symptom-based disorders (e.g., Zalta & Shankman, 2016). For example, more than 9,000 youths (ages 8 to 21 years) who had been hospitalized for diverse medical reasons were recruited for a recent project incorporating extensive data collection that included a neurocognitive test battery and a structured instrument for assessing psychiatric symptoms; the large sample size enabled the formation of 13 one-year age groupings (8–20) for analysis. Participants with psychotic symptoms showed consistent cognitive delays (compared with the typically developing group) across the entire age range, with particular deficits in complex cognition and social cognition (Gur et al., 2014). These data suggest that cognitive growth charts, analogous to developmental height-weight graphs, could provide data for etiological and prevention studies on both childhood-onset neurodevelopmental psychopathology and later early-adult disorders. In fact, an international group of investigators is beginning such a study in India, assessing six cognitive domains in young children with a developmental battery of gamified tasks (Mukherjee et al., 2020).
Measurement and assessment
The original goals of RDoC to foster integrative analyses across multiple units of measurement were in some respects aspirational because of the lack of appropriate analytic methodologies. However, computational methods for studying mental disorders (often termed computational psychiatry) have rapidly emerged as invaluable tools (Ferrante et al., 2019). This area is a priority for RDoC because it emphasizes multisystem integration, enhancing the ability to define psychophysiological constructs validated by quantitative analyses of relationships among various measurement systems (Sanislow et al., 2019).
Broadly speaking, two main types of computational approaches have been deployed (Huys, 2018). The first comprises theory-driven models, in which a paradigmatic model is tested to evaluate how closely its parameters fit observed data. Reinforcement-learning paradigms have been a productive exemplar of this approach; although basic experiments have focused on dopamine signaling and reward prediction errors (i.e., differences between received and predicted rewards), studies focusing on behavioral measures have been valuable for basic and clinical research (e.g., Barch et al., 2017). Devising valid and reliable computationally based measures of RDoC dimensions will be a high priority in coming years. Accordingly, NIMH has issued funding announcements both for validating brain-behavior relationships of existing behavioral tasks (MH-19-242) and for creating new behavioral tasks based on computational models (MH-19-240).
The second computational approach is generally termed “data driven” (Huys, 2018). As applied to RDoC, data-driven analyses are often used to derive potential clinical phenotypes via analyses of multiple response systems. B-SNIP (Bipolar-Schizophrenia Network for Intermediate Phenotypes) is one exemplary project. Analyses conducted on a large sample of patients with psychotic disorders (schizophrenia, schizoaffective disorder, and psychotic bipolar disorder) revealed three biotypes (clusters) based on measurements integrating event-related potentials and performance on cognitive tasks. Biotypes showed a stronger relationship to other measures, such as social functioning and gray-matter loss, than disorder categories did (Clementz et al., 2016). Such analyses not only are useful for generating new ideas about psychopathology and assessment, but also augur the possibility of new precision-medicine treatments derived from such biotypes (Sanislow et al., 2019).
Digital phenotyping is another rapidly emerging measurement approach with great promise for RDoC studies. Data are generated from smartphones and similar devices, mostly employing passive methods that do not disrupt ongoing behavior. Smart devices can gather a variety of data that are not available from other sources, such as location (GPS), number of social contacts, brief cognitive assessments, and increasing numbers of physiological measures (Torous et al., 2017). A different type of digital assessment uses natural language processing of various text materials. This work can include analyses of texts generated by participants, but other sources are also promising. A recent study demonstrated the feasibility of using natural language processing to extract measures of RDoC constructs from inpatients’ narrative chart notes. These measures predicted pertinent clinical outcomes, such as length of stay and increases in readmission risk (McCoy et al., 2018). These advanced technologies, combined with computational methods for analysis, have the potential to revolutionize the understanding of real-world behavior and its relationship to psychopathology on an individual basis.
Challenges
As would be expected of an experimental framework, RDoC has experienced numerous challenges of various types. Some of these involve misunderstandings about the framework and its process. For instance, some investigators have inferred that only constructs listed on the RDoC website can be used in RDoC-oriented grant applications; this is not the case, because (as noted above) the development of new (or revised) constructs is a high priority. Many other misapprehensions are addressed on RDoC’s FAQ web page (https://www.nimh.nih.gov/research/research-funded-by-nimh/rdoc/resources/rdoc-frequently-asked-questions-faq).
Less obvious issues have arisen for the set of constructs. One example concerns the granularity of constructs, given hierarchies of behavior and of neural systems (Kozak & Cuthbert, 2016). For example, in many areas of psychopathology research (e.g., schizophrenia), cognition is often studied at the level of the cognitive domain; however, successively finer-grained components also studied by scientists include executive function, working memory, and several subconstructs of working memory. The appropriate level of granularity for understanding real-world dysfunction or treatment interventions is not obvious, and likely varies across different domains and constructs, types of psychopathology, contexts, level of development, and other variables.
Another challenge relates to important issues of measurement and psychometrics. Strong relationships between different measurement systems (e.g., behavioral data and neurophysiological activity) have been observed in many studies of experimental groups. However, the import of such findings for theoretical or clinical use is weakened by the fact that these systems often demonstrate rather modest test-retest reliability at the individual level (Hedge et al., 2018). This challenge is, of course, not confined to RDoC, but is shared with the majority of contemporary research on mental disorders. There are no easy solutions for these issues, but research has brought progress in various areas. For instance, behavioral tasks are often developed to minimize between-subjects variability in order to highlight the nature of the function being studied, and this statistically results in low reliability across repeated tests; accordingly, developing and selecting tasks tailored to the study of individual differences may greatly enhance test-retest reliability (Hedge et al., 2018). Progress has also been made in mitigating similar challenges with neuroimaging (Etkin, 2019). New generations of behavioral tasks are moving toward shorter administrations (5 min or less) administered on mobile devices at the participant’s convenience, but with numerous test sessions across time; in this manner, more stable estimates can be established with potentially less sample attrition.
Summary and Conclusion
RDoC is fundamentally an experimental psychopathology initiative whose roots in psychological theory and measurement are clear—as illustrated by its incorporation of long-established research areas such as dimensional analyses, psychological constructs, and developmental trajectories. A key aspect concerns attention to mind-body issues that can begin to reconcile and integrate separate research traditions (e.g., phenomenology, behaviorism, neurobiology, and genetics) into a coherent view of mental disorders supported by empirical research.
This overview augurs the diverse palette of possibilities for RDoC’s next decade (Gordon, 2020b). It can be anticipated that research directions will evolve yet more rapidly as students trained in RDoC and related approaches build careers built on these concepts. It is difficult to predict how the field will change or the rate at which new ideas will be disseminated into clinical venues. In the longer term, even cloudier is the crystal ball regarding revisions to diagnostic manuals and corresponding alterations in the types of psychopathology for which specific treatments are approved by regulatory agencies. Extensive debate will be necessary, but the field now seems much more open to various possibilities. As NIMH Director Joshua Gordon (2020a) concluded in a recent message, “the RDoC framework has changed the conversation in mental health research” (para.7).
Recommended Reading
Beauchaine, T. P., & Hinshaw, S. P. (2020). (See References). Provides a review of RDoC-oriented developmental studies, discusses “misguided assumptions” that have slowed research to date (e.g., that biological measures should show 1:1 relationships with behavior), and outlines an agenda for incorporating RDoC principles in studies of developmental psychopathology.
Hedge, C., Powell, G., & Sumner, P. (2018). (See References). Discusses the problem of modest test-retest reliability frequently observed in studies of individual differences in psychopathology (and other areas), providing several illustrative examples and presenting alternative approaches to measurement, statistical analyses, and experimental designs.
Kozak, M. J., & Cuthbert, B. N. (2016). (See References). Presents a detailed summary of the RDoC initiative’s rationale, goals, framework, and process and discusses theoretical and methodological issues, such as mind-body relationships, reductionism, integrative analyses across types of measurement, and biomarkers.
Lake, J. I., Yee, C. M., & Miller, G. A. (2017). (See References). Provides a summary of the main strategic principles of the RDoC initiative and addresses critiques that reflect a number of common misperceptions about RDoC’s overall goals and scientific perspective, relationships across various classes of measurement, and approaches to defining and studying RDoC constructs.
Zalta, A. K., & Shankman, S. A. (2016). (See References). Presents a systematic analysis of ways in which RDoC’s dimensional approach can be utilized in the service of prevention research, describing the potential advantages compared with traditional diagnostic categories and outlining new criteria for prevention studies in an RDoC context, with an emphasis on the role of trials directed toward particular etiological mechanisms.
