The RDoC Project
The NIMH released a major update of its Strategic Plan in 2008. As part of this plan, two of the four major Strategic Objectives contained language referring to new initiatives that would link behavioral and physiological measures in order [to] study dimensions of functioning related to mental disorders.

The key statement was contained in Strategy 1.4 of the plan: “Develop, for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures” (NIMH, 2008).

Aim 1
“Initiate a process for bringing together experts in clinical and basic sciences to jointly identify the fundamental behavioral components that may span multiple disorders (e.g., executive functioning . . .) and that are more amenable to neuroscience approaches” (NIMH, 2008).

Aim 2
“Integrate the fundamental genetic, neurobiological, behavioral, environmental, and experiential components that comprise these disorders.”

Aim 3
“Determine the full range of variation, for normal to abnormal, among the fundamental components to improve understanding of what is typical versus pathological.”

RDoC inverts the usual paradigm for conceptualizing psychopathology: Rather than defining illnesses on the basis of presenting signs and symptoms and then searching for a disease-related problem, RDoC treats psychopathology in terms of varying degrees of dysregulation in normal-range functioning, considered from a relatively specific, construct-oriented focus. This approach has important ramifications for translational research in that studies of basic processes (in humans and nonhuman animals) can be applied much more directly to clinically significant issues (Anderzhanova, Kirmeier, & Wotiak, 2017).

Aim 4
“Develop reliable and valid measures of these fundamental components of mental disorders for use in basic studies and in more clinical settings.”

The binary, disease/no-disease legacy of the DSM/ICD system represents another issue.

At present it is widely recognized that we can make inferences about subjective experience from public data (self- report, overt behavior, biological measures), but the question of what role subjective experience should have in studying, understanding, preventing, and treating psychopathology and how to include it in rigorous science is not settled.

However, the philosophy of science literature has repeatedly shown those premises to be untenable even in principle across decades of evaluation.

As is often the case for relationships among various areas of science, important aspects of psychological science cannot be represented adequately in biological science. For instance, with respect to various levels of psychological and biological mechanisms, “macro findings are indispensable to explanations of phenomena of interest by (a) providing
information regarding higher levels of organization in mechanisms, (b) including information not contained within certain micro explanations and (c) providing more general and stable causal explanations relative to micro explanations in certain situations” (Sharp & Miller, 2019, p. 18; see also Miller, 2010, for extended critique, and Miller
& Bartholomew, 2020, for suggestions of further reading on this point).

The state of the art with respect to causation between biological and psychological events is such that there is not a single instance in which we have worked out the full causal chain.

An excellent example of this type [of research that groups participants into clusters based on a combination of variables] is the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) study, which involved two large cohorts—one initial sample and a replication sample, each with more than 700 patients and 200 or more controls—enrolling patients with either schizophrenia, schizoaffective disorder, or bipolar disorder (Clementz et al., 2021).

As [another] example, Kernbach et al. (2018) applied a sophisticated machine-learning algorithm to resting- state brain connectivity data gathered from a large sample of three groups of youth aged 7–21 (attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], or typically developing), which returned three factors of network connections whose combined effects were related dimensionally to both ADHD and ASD.