Computational Psychiatry

Mathematical Modeling of Mental Illness

Alan Anticevic, John D Murray

• Publisher: Academic Press
• Year: 2017
• ISBN:
  9780128098264
• Topic: Medicine
• Subtopic: Psychiatry & Mental Health

Computational Psychiatry: Mathematical Modeling of Mental Illness is the first systematic effort to bring together leading scholars in the fields of psychiatry and computational neuroscience who have conducted the most impactful research and scholarship in this area. It includes an introduction outlining the challenges and opportunities facing the field of psychiatry that is followed by a detailed treatment of computational methods used in the service of understanding neuropsychiatric symptoms, improving diagnosis and guiding treatments.

This book provides a vital resource for the clinical neuroscience community with an in-depth treatment of various computational neuroscience approaches geared towards understanding psychiatric phenomena. Its most valuable feature is a comprehensive survey of work from leaders in this field.

• Offers an in-depth overview of the rapidly evolving field of computational psychiatry
• Written for academics, researchers, advanced students and clinicians in the fields of computational neuroscience, clinical neuroscience, psychiatry, clinical psychology, neurology and cognitive neuroscience
• Provides a comprehensive survey of work from leaders in this field and a presentation of a range of computational psychiatry methods and approaches geared towards a broad array of psychiatric problems

Section III

Characterizing Complex Psychiatric Symptoms via Mathematical Models

Outline

Chapter 8. A Case Study in Computational Psychiatry: Addiction as Failure Modes of the Decision-Making System

Chapter 9. Modeling Negative Symptoms in Schizophrenia

Chapter 10. Bayesian Approaches to Learning and Decision-Making

Chapter 11. Computational Phenotypes Revealed by Interactive Economic Games

Chapter 8

A Case Study in Computational Psychiatry

Addiction as Failure Modes of the Decision-Making System

Cody J. Walters, and A.D. Redish University of Minnesota, Minneapolis, MN, United States

Abstract

We review a new perspective on addiction as due to failure modes of decision-making networks. In a sense, this suggests that addiction is a symptom that can arise from any of a number of potential underlying vulnerabilities. We identify four primary action-selection systems and review how multiple deficits (or “failure modes”) of these systems can lead to continued harmful dysfunction typically identified as addiction. These methods have shaped a new generation of tools for studying the etiology of neuropsychiatric dysfunction. These tools are aimed at identifying specific failure modes so that treatments can be individualized for specific patients. Moving beyond dysfunction, we also review how a computational understanding of treatment paradigms can reveal their interaction with these multiple decision systems, which can suggest ways to identify the patients most likely to be helped by treatments and ways to improve the treatments themselves.

Keywords

Addiction; Computational psychiatry; Decision-making; Gambling; Neuroeconomics; Substance abuse; Treatments for addiction

Outline

8.1 The Machinery of Decision-Making

8.2 Addiction as Failure Modes of Decision-Making Systems

8.3 Beyond Simple Failure Modes

8.4 Reliability Engineering

8.5 Implications for Treatment

8.6 Conclusions

References

Because addiction is so hard to define, the DSM-IV defined drug dependence and avoided the word addiction (DSM-IV-TR, 2000). However, more recent studies have suggested that addiction-like behaviors can underlie nondrug decision problems as well (Holden, 2001; Schüll, 2012; Robbins and Clark, 2015). But then we run into the problem of whether all continued behaviors are addictions. Do we really want to say that Brett Favre was “addicted” to football because he continued playing long after the game had damaged his body? Do we really want to say that Osip Mandelstam was “addicted” to poetry because he continued to write even after Stalin had sent him to the Gulag (where he eventually died)? To avoid this difficult definition, we will unask the question and instead concentrate on specific decision-making errors and relate that to problematic behaviors often categorized as addiction (Redish et al., 2008; Heyman, 2009, 2013; Redish, 2013).

Current models of psychiatry suggest that psychiatric disorders should be defined in terms of “harmful dysfunction” (Wakefield, 2007). This definition includes a scientific component (dysfunction) and a sociological component (harm). For example, illiteracy is harmful but is not usually considered a brain dysfunction. (On the other hand, dyslexia is both harmful and a brain dysfunction (Norton et al., 2015; Jaffe-Dax et al., 2015).) Synesthesia is due to a brain dysfunction but is not generally considered harmful (Cytowic, 1998). Treatment needs to be predicated on fixing those things that are harmful, but the appropriate treatment depends on the dysfunction. For example, most clinicians do not feel a need to treat synesthesia, but both dyslexia and illiteracy need treatment. Both of these problems require treatment, but because the causes are different, treatments for illiteracy and dyslexia will likely need to be different. In this chapter, we will make the case that addiction is a symptom, not a disease, and that because the underlying causes (the underlying dysfunctions) for addiction are varied the necessary treatments must be varied (Redish et al., 2008). We will make the case that rather than categorizing subjects in terms of their addiction (cocaine addiction, heroin addiction, gambling addiction), we should be defining them in terms of their decision-making dysfunctions (overvaluation, errors in expectation, reactions to anxiety). Lastly, we will argue that treatments should be guided by identifying the underlying dysfunction in an addict's decision-making circuitry to allow clinicians to individualize treatments.

A key concept that we will build this chapter on is that of a vulnerability or failure mode—a breakdown in a process due to a malfunctioning component. These terms come from the field of reliability engineering where one tries to identify the underlying breakdown that has caused a system-wide failure. A flat tire, for example, is a failure mode of automobiles (and bicycles) because a tire is a thin rubber tube filled with air. If that tube becomes punctured, then the air leaks out and the car is no longer riding on the normal air cushion. On the other hand, tank treads (which do not ride on air) are not susceptible to going flat (although they are vulnerable to being split). Just as cars a...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface
• Meeting Emerging Challenges and Opportunities in Psychiatry Through Computational Neuroscience
• Section I. Applying Circuit Modeling to Understand Psychiatric Symptoms
• Section II. Modeling Neural System Disruptions in Psychiatric Illness
• Section III. Characterizing Complex Psychiatric Symptoms via Mathematical Models
• Index