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This chapter begins with the assumptions of the present volume that a crisis exists in psychiatric research and that research concerning mental illness has entered a period of “extraordinary science.” After clarifying certain key features of both the crisis and extraordinary science, we examine the reasons for the crisis so as to identify some major challenges facing mental illness research during this period. We identify four broad classes of challenge: ideological, methodological, clinical, and transitional. We then articulate a version of the innovative research program of computational psychiatry that introduces novel representational and methodological resources and holds promise for meeting some of the various challenges. Finally, we demonstrate this promise using some concrete examples of research in this area concerning Parkinson’s Disease and Schizophrenia.

Psychiatry faces a number of challenges due largely to the complexity of the relationship between mind and brain. Starting from the now well-justified assumption that the mind is instantiated in the physical substrate of the brain, understanding this relationship is going to be critical to any understanding of function and dysfunction. Key to that translation from physical substrate to mental function and dysfunction is the computational perspective: it provides a way of translating knowledge and understanding between levels of analysis (Churchland and Sejnowski 1994). Importantly, the computational perspective enables translation to both identify emergent properties (e.g., how a molecular change in a receptor affects behavior) and consequential properties (e.g., how an external sociological trauma can lead to circuit changes in neural processing). Given that psychiatry is about treating harmful dysfunction interacting across many levels (from subcellular to sociological), this chapter argues that the computational perspective is fundamental to understanding the relationship between mind and brain, and thus offers a new perspective on psychiatry.

Psychiatry faces numerous challenges: the reconceptualization of symptoms and diagnoses, disease prevention, treatment development and monitoring of its effects, and the provision of individualized, precision medicine. To confront the complexity and heterogeneity intrinsic to brain disorders, psychiatry needs better biological, quantitative, and theoretical grounding. This chapter seeks to identify the sources of complexity and heterogeneity, which include the interplay between genetic and epigenetic factors with the environment and their impact on neural circuits. Computational approaches provide a framework to address complexity and heterogeneity, which cannot be seen as noise to be eliminated from diagnosis and treatment of disorders. Complexity and heterogeneity arise from intrinsic features of brain function, and thus present opportunities for computational models to provide a more accurate biological foundation for diagnosis and treatment of psychiatric disorders. Challenges to be addressed by a computational framework include: (a) improving the search for risk factors and biomarkers, which can be used toward primary prevention of disease; (b) representing the biological ground truth of psychiatric disorders, which will improve the accuracy of diagnostic categories, assist in discovering new treatments, and aid in precision medicine; (c) representing how risk factors, biomarkers, and the underlying biology change through the course of development, disease progression, and treatment process.

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.

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.

The application of computational neuroscience models to mental disorders has given rise to the emerging field of computational psychiatry. To date, however, there has been limited application of this approach to understanding the change process in psychotherapy. This chapter reviews leading approaches in computational neuroscience: predictive coding, active inference, and reinforcement learning. We then provide examples of how these complimentary approaches can be used to model a range of clinical phenomena and associated clinical interventions, including those associated with emotional awareness, specific phobia, maladaptive self-related beliefs, maladaptive repetitive behavior patterns, and the role of re-experiencing negative affect in the therapeutic process. The authors illustrate how this perspective can provide additional insights into the nature of the types of memories (cast as parameters in computational models) that maintain psychopathology, how they may be instantiated in the brain, and how new experiences in psychotherapy can alter/update these memories in a manner that can be quantitatively modeled. The authors conclude that the computational perspective represents a unique level of description that compliments that of the integrated memory model in a synergistic and informative manner.

The burden of depression is substantially aggravated by relapses and recurrences, and these become more inevitable with every episode of depression. This chapter describes how computational psychiatry can provide a normative framework for emotions and an integrative approach to core cognitive components of depression and relapse. Central to this is the notion that emotions effectively imply a valuation; thus they are amenable to description and dissection by reinforcement-learning methods. It is argued that cognitive accounts of emotion can be viewed in terms of model-based valuation, and that automatic emotional responses relate to model-free valuation and the innate recruitment of fixed behavioral patterns. This model-based view captures phenomena such as helplessness, hopelessness, attributions, and stress sensitization. Considering it in more atomic algorithmic detail opens up the possibility of viewing rumination and emotion regulation in this same normative framework. The problem of treatment selection for relapse and recurrence prevention is outlined and suggestions made on how the computational framework of emotions might help improve this. The chapter closes with a brief overview of what we can hope to gain from computational psychiatry.

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.

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.

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.

Psychiatry has found it difficult to develop a nosology that allows for the targeted treatment of disorders of the mind. This article sets out a possible way forward: harnessing systems theory to provide the conceptual constraints needed to link clinical phenomena with neurobiology. This approach builds on the insight that the mind is a system which, to regulate its environment, needs to have a model of that environment and needs to update predictions about it using the rules of inductive logic. It can be shown that Bayesian inference can be reduced to updating beliefs based on precision-weighted prediction errors, where a prediction error is the difference between actual and predicted input, and precision is the confidence associated with the input prediction. Precision weighting of prediction errors entails that a given discrepancy between outcome and prediction means more, and leads to greater belief updates, the more confidently the prediction was made. This provides a conceptual framework linking clinical experience with the pathophysiology underlying disorders of the mind. Limitations of this approach are discussed and ways to work around them illustrated. Initial steps and possible future directions toward a nosology based on failures of precision weighting are discussed.