research domain meaning

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What's the difference between 'research topic' and 'research area'?

I am writing an essay to apply for a summer research project and is supposed to write about 'general research topic that interests me' and 'area I would like to focus'. I'm kind of confused about these two terms. What's the difference?

For example, if I'm interested in computer science, where should I write it?

p.s. I have asked this question in English Language & Usage site but didn't get answer. So I suppose that these two words may only have difference in academic field?

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2 Answers 2

A research area is what a research topic is placed into, but is much broader than the scope of the topic. For example a research area can be human physiology, computer science (as you mentioned) or even relate to a specific field within these broader terms such as cardiac electrophysiology or machine learning respectively.

A research topic would be a specific question, hypothesis or problem you wish to investigate and answer which is under the scope of your research area. That is to say, my research area is in neuroscience/neurophysiology and my research topic is investigating the mechanisms of neuronal communication, as an example.

You would want to say topics that interest you which relate to a certain problem that you may be aware of, whereas in the research area you would want to outline your inclinations towards a particular field of academia.

While a topic is narrower than an area (for example, your area may be "solid state physics" and your topic "semiconductor tuning based on dopage"), it's probably true that for most people there is little difference between the two terms as far as colloquial usage is concerned.

In other words, don't obsess about the difference -- though, if you want, consider the "area" a broader term.

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Exploring the Different Domains of Research: An Overview

Have you ever wondered how researchers continue to push the boundaries of knowledge? From deciphering the mysteries of the human brain to unearthing ancient civilizations, the world of research is a captivating realm that knows no bounds. As we embark on an exploration of the different domains of research, we are bound to unravel the fascinating endeavors undertaken by ingenious minds. So, grab your intellectual compass, and let us delve into the vast and wondrous world of research together!

Definition of Research

In the context of "Domains of research", research can be defined as the systematic investigation and study of a subject or problem to generate new knowledge, validate existing theories, or explore unanswered questions. Some key characteristics of research include:

• Systematic approach : Research follows a structured and organized methodology to ensure reliability and validity.
• Objective inquiry : It involves the pursuit of unbiased and evidence-based information, devoid of personal biases or opinions.
• Exploration of knowledge gaps : Research aims to fill gaps in current understanding by exploring new ideas or testing hypotheses.
• Rigorous analysis : Data collection and analysis methods are carefully designed and executed to ensure accuracy and reliability .

For example, in scientific research, investigations are conducted to expand our understanding of natural phenomena or develop new technologies. Similarly, educational research focuses on studying teaching methods and student learning outcomes to improve educational practices.

Importance of Research

Research plays a vital role across various domains, providing valuable insights and driving progress. In the field of medicine, research leads to the development of new treatments and improves patient outcomes. In business, research helps companies understand consumer needs, market trends, and optimize strategies for growth . In education, research informs teaching methods, curriculum development, and enhances student learning.

Social research uncovers patterns and trends in society, assisting policymakers in making informed decisions. By exploring different domains of research, we gain a deeper understanding of the world around us, allowing us to make informed decisions and drive positive change.

Overview of Research Domains

• Research domains encompass various fields of study that contribute to the advancement of knowledge and understanding in different areas.
• They provide specialized frameworks for conducting research and generating insights within specific disciplines.
• Examples of research domains include scientific research, social research, medical research, educational research, and business research.
• Scientific research focuses on acquiring empirical evidence and conducting experiments in fields such as physics, biology, and chemistry.
• Social research delves into human behavior, societal patterns, and cultural phenomena to gain insights into social dynamics.
• Medical research aims to enhance medical knowledge, discover new treatments, and improve healthcare practices.
• Educational research studies methods, practices, and theories related to teaching and learning.
• Business research investigates various aspects of the business world, such as market trends, consumer behavior, and organizational strategies.

Through exploration and collaboration across these domains, researchers contribute to continuous advancements in their respective fields, enabling society to address complex challenges and facilitate progress.

Scientific Research

Definition of scientific research.

Scientific research involves the systematic investigation of phenomena using rigorous methodologies to generate new knowledge. It focuses on verifying or refuting existing theories and hypotheses through empirical evidence. Scientists conduct controlled experiments, collect data, and analyze results to draw conclusions.

For example, in the field of biology, researchers may study the impact of a specific drug on a particular disease by conducting experiments on cells or animals. In physics, scientists may explore the properties of materials by conducting experiments in laboratories. Scientific research aims to expand our understanding of the natural world and provide a foundation for further advancements and innovations.

Examples of Scientific Research

Scientific research encompasses a wide range of disciplines and areas of study. In the field of biology, for example, scientists conduct research to understand the mechanisms of cell division. This knowledge can then be applied to develop new treatments for diseases such as cancer. In the field of physics, scientists conduct experiments to explore the properties of matter and energy, leading to the development of technologies like lasers and magnetic resonance imaging (MRI).

Scientific researchalso extends to fields like psychology, sociology, and environmental science, where researchers investigate topics such as human behavior, social trends, and climate change. By exploring these different domains of research, scientists gain valuable insights that contribute to the advancement of knowledge and the betterment of society.

Contributions of Scientific Research

Scientific research contributes to advancements in various fields. In medicine, it helps uncover new treatments and drugs, improving patient outcomes . In technology, research drives innovation, leading to the development of new devices and technologies. In environmental studies, research provides insights into the impacts of human activity, helping create sustainable practices.

Additionally, scientific research expands our knowledge of the natural world and enables us to better understand complex phenomena.

For example, studying climate patterns assists in predicting and mitigating natural disasters.

Social Research

Definition of social research.

Social research refers to the systematic investigation and study of human behavior, interactions, and social phenomena. It aims to understand and analyze various aspects of society, including social structures, relationships, norms, and beliefs. This type of research involves collecting and analyzing data through methods such as surveys, interviews, observations, and experiments. By examining social issues and trends, social research provides valuable insights into how society functions and changes over time. It helps researchers identify patterns, understand social processes, and make informed decisions in fields like sociology, anthropology, psychology, and public policy. For example:

• Studying the impact of social media on youth mental health.
• Investigating the effectiveness of diversity and inclusion programs in workplaces.
• Examining the social factors influencing voter behavior during elections.

Examples of Social Research

Social research encompasses a wide range of study areas, which can provide valuable insights into various aspects of society. Some examples of social research include:

• Surveys : Conducting surveys to gather data on public opinions, attitudes, and behaviors towards specific social issues.
• Observational Studies : Observing and analyzing social interactions in natural settings, such as studying patterns of communication in a workplace or the behavior of individuals in a public space.
• Case Studies : Researching specific social phenomena or events in detail, such as investigating the impact of a community development program on local residents.
• Ethnographic Research : Immersing in a culture or community to understand its social dynamics, norms, and practices. This can be done through participant observation and interviews.
• Historical Analysis : Analyzing historical documents, records, and archival data to examine social trends, changes, and their impacts over time.

Each of these methodologies can provide valuable insights into social issues, behaviors, and trends, enabling researchers to better understand and address societal challenges.

Applications of Social Research

Social research has practical applications in various fields. In the realm of public policy, it helps inform decision-making by providing evidence-based insights into social issues such as poverty, education, and healthcare.

For example, research on the effectiveness of community-based interventions can guide policymakers in designing targeted programs to address societal challenges. In marketing and consumer behavior, social research assists in understanding consumer preferences and trends, aiding companies in developing effective marketing strategies. Moreover, social research supports the development of social interventions aimed at improving outcomes in areas like mental health, crime prevention, and social justice. By uncovering actionable insights, social research contributes to creating positive change in society.

Medical Research

Definition of medical research.

Medical research refers to the systematic investigation of health science topics  to enhance our understanding of diseases, medical treatments, and patient care. This domain of research plays a significant role in advancing medical knowledge and improving healthcare practices. Key aspects of the definition of medical research include:

• Conducting controlled experiments and clinical trials to evaluate the safety and efficacy of new drugs and medical interventions.
• Collecting and analyzing data from patient populations to identify risk factors, disease prevalence, and treatment outcomes.
• Investigating the underlying mechanisms of diseases to develop innovative diagnostic tools and therapeutic approaches.
• Collaborating with healthcare professionals, scientists, and institutions to foster interdisciplinary research and facilitate evidence-based medical practices.

By defining medical research, we can appreciate its contribution to enhancing medical knowledge, improving patient outcomes, and shaping medical policies and guidelines.

Examples of Medical Research

Medical research encompasses a wide range of studies focused on improving human health. Some examples include clinical trials investigating new treatments or drugs for specific diseases, epidemiological research exploring patterns and causes of diseases in certain populations, and genetic research aiming to understand the genetic basis of diseases.

Another important area is translational research, which aims to bridge the gap between scientific discoveries and practical applications in healthcare. For instance, studies that focus on developing new medical technologies or diagnostic tools fall under this domain. Medical research plays a vital role in advancing our understanding of diseases, improving healthcare practices, and ultimately saving lives.

Impact of Medical Research on Healthcare

Medical research plays a significant role in shaping the healthcare landscape. It drives advancements in treatments, diagnostic methods, and disease prevention strategies.

For example, ongoing research on cancer has led to the development of targeted therapies that effectively attack specific tumor cells while minimizing damage to healthy tissues. Similarly, research on infectious diseases enables the identification of new vaccines and the improvement of existing ones, contributing to the control and eradication of outbreaks. Medical research also helps healthcare professionals make evidence-based decisions, ensuring patients receive the most appropriate and effective care. By continuously pushing the boundaries of medical knowledge, research enhances patient outcomes and empowers healthcare providers to deliver high-quality, informed care.

Educational Research

Definition of educational research.

Educational research encompasses the systematic study of educational systems, processes, and outcomes. It involves investigating various aspects of teaching and learning to enhance educational practices and improve student outcomes. Researchers use different methodologies, such as surveys, interviews, and observations, to collect data and analyze trends and patterns in education.

For example, educational research may explore the impact of technology on student engagement or the effectiveness of different teaching strategies. By conducting educational research, educators can make informed decisions and implement evidence-based practices to elevate the quality of education and foster student success.

Examples of Educational Research

Educational research encompasses various areas of study and aims to improve educational practices and outcomes. Some examples of educational research include investigations into effective teaching methods, evaluating the impact of new educational technologies, and analyzing the effectiveness of different curriculum designs.

Researchers may also explore strategies for enhancing student engagement and motivation, investigating the benefits of inclusive education, or examining the role of parentalinvolvement in student success. These research findings provide valuable insights for educators, policymakers, and educational institutions to make informed decisions and implement evidence-based practices that positively impact teaching and learning.

Improving Education Systems through Research

Research plays a significant role in enhancing education systems. By conducting comprehensive studies, researchers can identify effective teaching methods, curriculum enhancements, and student support systems.

For example, research on personalized learning has helped educators tailor instruction to individual student needs, improving academic outcomes.

Additionally, studies on the impact of technology in education have led to the development of innovative tools and digital resources that enhance learning experiences. By applying research findings, educational institutions can make informed decisions to optimize teaching practices, foster student engagement, and promote better learning outcomes.

Business Research

Definition of business research.

Business research refers to systematic investigation and analysis conducted to gain insights into various aspects of the business world. It involves collecting and interpreting data to inform decision-making and address challenges or opportunities within organizations. Business research encompasses a wide range of activities, including market research, competitor analysis, customer satisfaction surveys, and trend analysis.

For example, organizations may use business research to understand consumer preferences, evaluate the success of marketing campaigns, or identify areas for process improvement. By conducting research in the business domain, organizations can make informed decisions, enhance their strategies, and stay competitive in the market.

Examples of Business Research

Business research encompasses a wide range of areas, from market analysis to consumer behavior. One example of business research is conducting surveys or interviews to gather insights on customer preferences and buying patterns. This information can help companies make informed decisions regarding product development, pricing strategies, and marketing campaigns. Another example is analyzing industry trends and competitors to identify opportunities for growth and stay ahead of the competition.

Benefits of Business Research for Organizations

Business research offers numerous benefits to organizations.

Firstly, it enables companies to gain valuable insights into market trends, customer preferences, and competitors. By conducting thorough market research, companies can identify gaps in the market and develop innovative products or services to meet customer demands.

Additionally, business research helps in assessing the effectiveness of marketing strategies and advertising campaigns, allowing organizations to make data-driven decisions for improved customer targeting and engagement. Furthermore, research helps organizations identify potential risks and challenges, enabling them to develop contingency plans and mitigate potential threats.

Wrapping up

This article provides a comprehensive overview of the various domains of research. It highlights the multidisciplinary nature of research and emphasizes the importance of exploring different fields to foster innovation and progress. The author discusses key domains such as social sciences, natural sciences, humanities, and engineering, shedding light on their unique characteristics and methodologies.

By understanding the diverse areas of research, researchers can collaborate more effectively and approach problems from multiple perspectives, driving advancements in various disciplines.

Research Methodology and Study Domain

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• S. W. F. Omta 2  

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This Section provides the theoretical foundation for this monograph. This chapter concentrates on the research methodology and study domain. A distinction is made between the trajectories of theory construction and theory application. The attention is then focused on the study domain. A broad definition of R&D is given, and it is classified into basic research, applied research and experimental development. After a factual overview at Dutch R&D in general, it focuses on biomedical research in particular. Because of its multinational character, the features of industrial pharmaceutical innovation are discussed on a more global level. In the next two chapters, the concepts of systems theory on which this study is based, are discussed in more detail. Chapter 2 concentrates on structure, behaviour and control at the level of the whole organization, and chapter 3 focuses on the object of study, i.e. the research unit level.

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Omta, S.W.F. (1995). Research Methodology and Study Domain. In: Critical Success Factors in Biomedical Research and Pharmaceutical Innovation. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0101-1_2

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Research Domain Criteria (RDoC): Progress and Potential

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 disorder categories constituted heterogeneous syndromes rather than specific diseases. However, established practices in peer review placed high priority on extant disorders in evaluating grant applications for mental illness. To provide guidelines for alternative study designs, NIMH included 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 as characterized by related behavioral/psychological and neurobiological data (e.g., reward valuation, working memory). RDoC also emphasizes full-range dimensional approaches (from typical to increasingly abnormal), neurodevelopment and environmental effects, and research designs that integrate data across behavioral, biological, and self-report measures. This commentary 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.

INTRODUCTION

The Research Domain Criteria (RDoC) project represents a framework for research on mental disorders that focuses on dimensions of behavioral/psychological functioning and their implementing neural circuits. RDoC originated from one element of the National Institute of Mental Health (NIMH) Strategic Plan for Research released in 2008, 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; American Psychiatric Association, 2013 ). Its fundamental architecture remains based upon the DSM-III of 1980, with disorder categories defined primarily by sets of presenting 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 research methodologies such as neuroimaging, sophisticated behavioral science, and genetics ( Kapur, Phillips, & Insel, 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” ( NIMH, 2008 ).

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. As a former NIMH Director summarized the issue: “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).

Put in another way, scientific review depends upon 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 comparing a single DSM patient group (e.g., bipolar disorder) to healthy controls; 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 to eventuate in symptoms and impairment.

The framework has been extensively described elsewhere ( Cuthbert & Kozak, 2013 ; Kozak & Cuthbert, 2016 ; National Institute of Mental Health, 2021 ) and is only summarized briefly here (see Figure 1 ). Constructs are the main focus of experimental attention, similar to the usual notion of psychological constructs but defined in terms of empirical evidence for both a basic functional dimension of behavior or psychological processes and for a neural circuit or system implementing the function. Constructs are nested in six broad domains (Negative Valence Systems, Positive Valence Systems, Cognitive Systems, Social Processes, Arousal and Regulatory Systems, and Sensorimotor Systems); for instance, the Cognitive Systems domain includes such constructs as Attention, Cognitive Control, and Perception.

Major elements of the RDoC framework. Domains represent the major focus of study, with each domain containing three to six related constructs (and sub-constructs in some instances); the vertical blue bars ranging from Genes to Self-Reports depict examples of various Units of Analysis that might be incorporated in RDoC studies. The Neurodevelopment arrow represents a life span approach, starting with conception and continuing through the stages of adulthood; Environment is used as a catch-all term for all potential aspects that might be included in study designs, such as family, schools, neighborhoods, and culture, but also including individual events such as accidents or assaults. Inclusion of one or both of these latter elements is encouraged in study designs to examine the context in which domains and constructs are studied.

Experimental designs typically focus on one or two constructs, resulting in studies that 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 as to explicate transitions from healthy to increasingly abnormal performance. RDoC prioritizes research that includes a substantial proportion of treatment-seeking participants (and controls with a range of sub-syndromal psychopathology); experiments comprised only of normal-range subjects are typically not considered as RDoC projects (although potentially appropriate for basic-science grant applications).

Investigators are encouraged to acquire data from multiple response systems (termed “Units of Analysis,” e.g., circuit measures, behavioral performance, self-reports) in order to address the well-known lack of coherence among measurement classes by conducting integrative analyses across systems. High priority is placed upon neurodevelopmental studies and environmental effects. An essential point is that both the domains/constructs and Units of Analysis are considered to be heuristic exemplars rather than fixed sets, as fostering development of new or refined domains/constructs and innovative measurement techniques are salient; it is the principles of the framework that comprise its capstone rather than current specific elements.

The RDOC project has attracted considerable attention regarding all aspects of mental disorders, including research, clinical issues, 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 NIH RePORTER grants database indicates nearly 500 active grants involving reference to RDoC, funded mostly by NIMH but other institutes as well. A Google search returns over 150,000 hits (as of June 2021), comprising a broad mix of scientific reports, commentaries, clinical considerations, and various blogs that all 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-5, and other efforts to develop alternative conceptual and research approaches to psychopathology – e.g., Borsboom, 2017 ; Kotov et al., 2017 ). As one 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 three-year epochs from 2009 to 2020 returned 92, 308, 953, and 1,745 hits, respectively.

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 ). While many factors slow progress in clinical innovation (such as insurance reimbursements tied to traditional diagnoses), consideration of more precise assessments and treatments is underway.

RDoC principles have also extended into clinical treatment studies, arriving sooner than expected due to the departure of pharmaceutical companies from drug development for mental disorders due to familiar problems of heterogeneity and co-morbidity. Constructs that attempt 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 clinical outcomes.

Reductionism and Mind-body issues

Early papers 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, multi-systems 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, Yee, & Miller, 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 with considerable overlaps among relevant categorical disorders. 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 ). Communications with the field in this area have focused on ways of depicting RDoC constructs across development, given evolving trajectories of psychological/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 in binary (well/sick), symptom-based disorders (e.g., Zalta & Shankman, 2016 ). For example, a recent project recruited over 9,000 youth (aged 8 to 21 years) hospitalized for diverse medical reasons, obtaining extensive data collection that included a neurocognitive test battery and a structured instrument for psychiatric symptoms; the large sample size enabled the formation of 13 one-year groupings (age 8 to 20) for analysis. Participants with psychotic symptoms showed consistent cognitive delays (compared to 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 both for child-onset neurodevelopmental psychopathology and for later early-adult disorders. In fact, an international group of investigators is beginning the implementation of such a concept 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 conduct integrative analyses across multiple units of measurement were in some respects aspirational due to 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., 2018 ). This area represents a priority for RDoC due to its emphasis on multi-system integration, enhancing the ability to define psychophysiological constructs validated by quantitative analyses of relationships among various measurement systems ( Sanislow, Ferrante, Pacheco, Rudorfer, & Morris, 2019 ). In fact, these features of RDoC played a palpable role in stimulating the development of computational approaches to mental disorders ( Marquand, Rezek, Buitelaar, & Beckmann, 2016 ).

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 in testing model paradigms to date; while basic experiments have focused on dopamine signaling and reward prediction errors, 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 represents a high priority in coming years. Accordingly, NIMH has issued funding announcements both for validating brain-behavior relationships of RDoC tasks (MH-19–242) and for the creation of new behavioral tasks based on computational models (MH-19–240).

The second set of computational tools is generally termed a data-driven approach ( Huys, 2018 ). As applied to RDoC, data-driven analyses are often used to derive potential clinical phenotypes via analyses of multiple response systems. One exemplary study to date has been the B-SNIP (Bipolar-Schizophrenia Network for Intermediate Phenotypes) 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 cognitive task measures and event-related potentials. Biotypes showed a stronger relationship to other measures, such as social functioning and gray matter loss, than disorder categories ( Clementz et al., 2016 ). Such analyses are not only useful for 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 comprises another rapidly emerging measurement class with great promise for RDoC studies. Data are generated from smart phones 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 GPS for assessing behavior, number of social contacts, brief cognitive assessments, and increasing numbers of physiological measures ( Torous, Onnela, and Keshavan, 2017 ). A different type of digital assessment comes from natural language processing (NLP) of various text materials. These can include analyses of texts generated by participants, but other sources are also promising. A recent study demonstrated the feasibility of NLP to extract measures of RDoC domains from narrative inpatient chart notes, which 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.

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 web site can be used in RDoC-themed applications; this is not the case, since (as noted above) the development of new (or revised) constructs is a high priority. Many other misapprehensions are clarified on the FAQ section of the RDoC web page ( NIMH, 2021 ).

Less obvious issues have also arisen for the set of constructs. One example concerns the granularity of constructs, given hierarchies of behavior and of neural systems ( Kozak & Cuthbert, 2016 ). As one example, cognition in many areas (e.g., schizophrenia) is often studied at the cognitive domain level; however, successively finer-grained components also studied by scientists include executive function, working memory, and several sub-components of working memory (as noted in the RDoC matrix). The appropriate level of granularity for understanding real-world dysfunction or treatment interventions is not obvious, and likely varies across different domains/constructs, types of psychopathology, contexts, level of development, and other variables.

Another challenge relates to important issues of measurement and psychometrics. Group effects relating different measurement systems have shown strong relationships in innumerable studies. However, many behavioral tasks and neuroimaging results demonstrate rather modest test-retest reliability ( Hedge et al., 2018 ). This challenge is of course not confined to RDoC, but is shared with the majority of contemporary research for mental disorders. There are no easy solutions for these issues, but research in various areas has progressed. For instance, behavioral tasks are often developed to minimize between-subject variability in order to highlight the nature of the function being studied, which statistically results in low reliability across repeated testings; 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 (five minutes or less) administered on mobile devices at the subject’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

As this overview of activities across its first decade illustrates, 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.

The sections above augur 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, and the rate at which ideas continue disseminating into clinical venues. In the longer term, the crystal ball is yet cloudier in terms of revisions to diagnostic manuals and corresponding alterations in approved indications for regulatory agencies. Extensive debate will be necessary, but the field now seems much more open to various possibilities. As NIMH Director Joshua Gordon concluded in a recent message, “… the RDoC framework has changed the conversation in mental health” ( Gordon, 2020a ).

Acknowledgments

Thanks are expressed to Dr. Uma Vaidyanathan for data regarding increases over time in the “transdiagnostic” keyword, part of a larger analysis examining recent secular changes in the translational research literature.

Disclaimer: The author reports no financial conflicts of interest.

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The NIMH Research Domain Criteria (RDoC) Project: Precision Medicine for Psychiatry

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Research Domain Criteria: Strengths, Weaknesses, and Potential Alternatives for Future Psychiatric Research

Affiliations.

• 1 Division of Neurobiology, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
• 2 Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
• 3 Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
• 4 Department of Pharmacology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
• 5 Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
• PMID: 31768375
• PMCID: PMC6873013
• DOI: 10.1159/000501797

The Research Domain Criteria (RDoC) paradigm was launched 10 years ago as a superior approach for investigation of mental illness. RDoC conceptualizes normal human behavior, emotion, and cognition as dimensional, with mental illnesses as dimensional extremes. We suggest that RDoC may have value for understanding normal human psychology and some conditions plausibly construed as extremes of normal variation. By contrast, for the most serious of mental illnesses, including dementia, autism, schizophrenia, and bipolar disorder, we argue that RDoC is conceptually flawed. RDoC conflates variation along dimensional axes of normal function with quantitative measurements of disease phenotypes and with the occurrence of diseases in overlapping clusters or spectra. This moves away from the disease model of major mental illness. Further, RDoC imposes a top-down approach to research. We argue that progress in major mental illness research will be more rapid with a bottom-up approach, starting with the discovery of etiological factors, proceeding to investigation of pathogenic pathways, including use of cell and animal models, and leading to a refined nosology and novel, targeted treatments.

Keywords: Autism; Bipolar disorder; Category; Diagnosis; Dimension; Gene-environment interaction; Genetics; National Institute of Mental Health; Nosology; Psychosis; Research Domain Criteria; Schizophrenia; Spectrum.

Copyright © 2019 by S. Karger AG, Basel.

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Conflict of interest statement

Dr. Ross reports grant support from NIH and CHDI, previous support from JNJ/Janssen, Teva, and Raptor/Horizon, clinical trial support from Teva, Vaccinex, Roche/Genentech, and CHDI, and consulting for Teva, Sage, uniQure, Roche/Ionis, and HSG. Dr. Margolis reports grant support from the NIH, ABCD Charitable Trust, and Teva.

Bimodality of the superficially unimodal…

Bimodality of the superficially unimodal Gaussian distribution of IQ in the population. In…

Idealized natural histories of forms…

Idealized natural histories of forms of psychotic disorder. These schematics are based on…

The top-down approach of RDoC.…

The top-down approach of RDoC. The RDoC approach to psychiatry begins with arbitrary…

Disjunction between phenomenology of psychosis…

Disjunction between phenomenology of psychosis and “dimensions” of RDoC. The “spectrum” of psychosis…

Relationship between genetic and environmental…

Relationship between genetic and environmental risk factors and clinical phenotypes. Genetic and environmental…

Hypothetical bottom-up scheme for conceptualizing…

Hypothetical bottom-up scheme for conceptualizing the causal chain from genetic etiologies to clinical…

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