Call for Pragmatic Computational Psychiatry

Biological psychiatry is at an impasse. Despite several decades of intense research, few, if any, biological parameters have contributed to a significant improvement in the life of a psychiatric patient. It is argued that this impasse may be a consequence of an obsessive focus on mechanisms. Alternatively, a risk prediction framework provides a more pragmatic approach, because it aims to develop tests and measures which generate clinically useful information. Computational approaches may have an important role to play here. This chapter presents an example of a risk-prediction framework, which shows that computational approaches provide a significant predictive advantage. Future directions and challenges are highlighted.

A Novel Framework for Improving Psychiatric Diagnostic Nosology

This chapter proposes a new framework for diagnostic nosology based on Bayesian principles. This novel integrative framework builds upon and improves the current diagnostic system in psychiatry. Instead of starting from the assumption that a diagnosis describes a specific unitary dysfunction that causes a set of symptoms, it is assumed that the underlying disease causes the clinician to make a diagnosis. Thus, unlike the current diagnostic system, this framework treats both symptoms and diagnostic classification as consequences of the underlying pathophysiology. Comorbidities are therefore easily incorporated into the framework and inform, rather than hinder, the diagnostic process. Further, the proposed framework provides a bridge that links putative constructs related to pathophysiology and clinical diagnoses related to signs and symptoms. Crucially, this novel framework explicitly provides an iterative approach, updating and selecting the best model, based on the highest-quality available evidence at any point. It can account for and incorporate the longitudinal course of an illness. This chapter details its theoretical basis and provides clinical examples to illustrate its utility and application. It is hoped that the framework will enhance our understanding of individual differences in brain function and behavior and ultimately improve treatment outcomes in psychiatry.

Current State of Psychiatric Nosology

Psychiatric classifications categorize how patients present to mental health care professionals and are necessarily utilitarian. From the clinician’s perspective, the most important goal of a psychiatric classification is to assist them in managing their patients’ psychiatric conditions by facilitating the selection of effective interventions and predicting management needs and outcomes. Due to the field’s lack of understanding of the neurobiological mechanisms underlying the psychiatric disorders in both the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD), diagnosis and treatment are only loosely related, thus limiting clinical utility. Both DSM and the chapter on mental and behavioral disorders in ICD adopted a descriptive atheoretical categorical approach that defines mental disorders according to syndromal patterns of presenting symptoms. This chapter discusses the fundamental challenges that underlie this decision. It then reviews the Research Domain Criteria (RDoC) project, a research framework established by the U.S. National Institute of Mental Health (NIMH) to assist researchers in relating the fundamental domains of behavioral functioning to their underlying neurobiological components. Designed to support the acquisition of knowledge of causal mechanisms underlying mental disorders, RDoC may facilitate a future paradigm shift in the classification of mental disorder.

Computational Cognitive Neuroscience Approaches to Deconstructing Mental Function and Dysfunction

Advances in our understanding of brain function and dysfunction require the integration of heterogeneous sources of data across multiple levels of analysis, from biophysics to cognition and back. This chapter reviews the utility of computational neuroscience approaches across these levels and how they have advanced our understanding of multiple constructs relevant for mental illness, including working memory, reward-based decision making, model-free and model-based reinforcement learning, exploration versus exploitation, Pavlovian contributions to motivated behavior, inhibitory control, and social interactions. The computational framework formalizes these processes, providing quantitative and falsifiable predictions. It also affords a characterization of mental illnesses not in terms of overall deficit but rather in terms of aberrations in managing fundamental trade-offs inherent within healthy cognitive processing.

Computational Approaches for Studying Mechanisms of Psychiatric Disorders

Vast spectra of biological and psychological processes are potentially involved in the mechanisms of psychiatric illness. Computational neuroscience brings a diverse toolkit to bear on understanding these processes. This chapter begins by organizing the many ways in which computational neuroscience may provide insight to the mechanisms of psychiatric illness. It then contextualizes the quest for deep mechanistic understanding through the perspective that even partial or nonmechanistic understanding can be applied productively. Finally, it questions the standards by which these approaches should be evaluated. If computational psychiatry hopes to go beyond traditional psychiatry, it cannot be judged solely on the basis of how closely it reproduces the diagnoses and prognoses of traditional psychiatry, but must also be judged against more fundamental measures such as patient outcomes.

Clinical Heterogeneity Arising from Categorical and Dimensional Features of the Neurobiology of Psychiatric Diagnoses

Clinical heterogeneity presents important challenges to optimizing psychiatric diagnoses and treatments. Patients clustered within current diagnostic schema vary widely on many features of their illness, including their responses to treatments. As outlined by the American Psychiatric Association Diagnostic and Statistical Manual (DSM), psychiatric diagnoses have been refined since DSM was introduced in 1952. These diagnoses serve as the targets for current treatments and supported the emergence of psychiatric genomics. However, the Research Domain Criteria highlight DSM’s shortcomings, including its limited ability to encompass dimensional features linking patients across diagnoses. This chapter considers elements of the dimensional and categorical features of psychiatric diagnoses, with a particular focus on schizophrenia. It highlights ways that computational neuroscience approaches have shed light on both dimensional and categorical features of the biology of schizophrenia. It also considers opportunities and challenges associated with attempts to reduce clinical heterogeneity through categorical and dimensional approaches to clustering patients. Finally, discussion will consider ways that one might work with both approaches in parallel or sequentially, as well as diagnostic schema that might integrate both perspectives.

Candidate Examples for a Computational Approach to Address Practical Problems in Psychiatry

Scientists and clinicians can utilize a model-based framework to develop computational approaches to psychiatric practice and bring scientific discoveries to a clinical interface. This chapter describes a general modeling perspective, which complements those derived in previous chapters, and provides distinct examples to highlight the scientific and preclinical research that can evolve out of a computational framework to offer new tools for clinical practice. It begins by reviewing areas of theoretical and modeling studies that have reached a critical mass and outlines the pathophysiological insights that have been revealed. The phasic dopamine temporal difference model shows how neurophysiological and neuroanatomical research, incorporated into a learning circuit model, provides a constrained hypothesis testing framework, related to the likely multiple mechanisms contributing to addiction. A potential application of generative models of neuroimaging measurements (dynamic causal models of EEG data) is described to predict individual treatment responses in patients with schizophrenia. The third example offers a novel approach to quantifying patient outcomes under a “recovery model” of psychiatric illness. In conclusion, consideration is given to the community efforts needed to support the validation of these and future applications.

What Does Computational Psychiatry Need to Explain to Capture Mechanisms of Psychopathology?

This chapter provides specific research examples on the neurobiology of mental illness—using psychosis as a case in point—that may begin to rise to the level of “facts,” or at least “almost facts” or strong “hints,” about important etiological mechanisms that need to be explained to capture key components of at least some facets of mental illness. These examples are then used to illustrate where computational psychiatry approaches may help. In particular, there is an opportunity to provide links across different levels of analysis (e.g., behavior, systems level, specific circuits and even genetic influences) in ways that can lead to a more unified framework for understanding the apparent multitude of impairments present in psychosis, which may in turn lead to the identification of new treatment or even prevention targets. This chapter also discusses some of the known conundrums about the etiology of mental illness that need to be accounted for in computational frameworks, including the presence of heterogeneity within current diagnostic categories, the vast degree of comorbidity across current diagnostic categories, and the need to reconceptualize the dimensionality versus categorical nature of mental illness.

On the Cusp

Modern psychiatry seeks to treat disorders of the brain, the most complex and least understood organ in the human body. This complexity poses a set of challenges that make progress in psychiatric research particularly difficult, despite the development of several promising novel avenues of research. New tools that explore the neural basis of behavior have accelerated the discovery in neuroscience, yet discovery into better psychiatric treatments has not kept pace. This chapter focuses on this disconnect between the challenges and promises of psychiatric neuroscience. It highlights the need for diagnostic nosology, biomarkers, and better treatments in psychiatry, and discusses three promising conceptual advances in psychiatric neuroscience. It holds that rigorous theory is needed to address the challenges faced by psychiatrists.

From Psychiatry to Computation and Back Again

In the opening chapters of this volume, we outlined a series of challenges facing psychiatry, as well as a description of its various promises, and suggested that taking a computational perspective could potentially illuminate a way forward. In this concluding chapter, we revisit these challenges and promises, in the context of what transpired at this Ernst Strüngmann Forum, to highlight the connections between the various themes raised. In particular, we will bring out the points of agreement and disagreement between the discussion groups and the chapters that arose from those discussions. We conclude with a description of the efforts, current and ongoing, to bring the potential synergy between psychiatry and computational neuroscience emphasized in this volume to a reality in the scientific and clinical arenas.