Computational Psychiatry: Combining Multiple Levels of Analysis to Understand Brain Disorders
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Computational Psychiatry: Combining multiple levels of analysis to understand brain disorders. by Thomas Viktor Wiecki Diploma, University of T¨ubingen, January 2010 M. Sc., Brown University, May 2012 A Dissertation submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in the Cognitive, Linguistic & Psychological Sciences at Brown University Providence, Rhode Island May 2015 Copyright 2015 by Thomas Viktor Wiecki This dissertation by Thomas Viktor Wiecki is accepted in its present form by the Cognitive, Linguistic & Psychological Sciences as satisfying the dissertation requirement for the degree of Doctor of Philosophy. Date 9/23/2014 Michael J. Frank, Director Recommended to the Graduate Council Date Thomas Serre, Reader Date 9/24/2014 Erik Sudderth, Reader 9-25-14 Date Benjamin Greenberg, Reader Approved by the Graduate Council Date Peter M. Weber, Dean of the Graduate School iii Acknowledgements This dissertation would not have been possible without the support of many people and institutions throughout the last several years. First and foremost I am grateful to my supervisor and friend Michael J Frank who contributed to my development as a researcher in a major way. I am also thankful to the other members of my thesis committee – Thomas Serre, Erik Sudderth and Ben Greenberg – for thoughtful feedback. Daniel Dillon, as well as Sara Tabrizi, Chrystalina Antoniades, Chris Kennard, Beth Borowsky, Monica Lewis, and Mina Creathorn deserve my gratitude for generously sharing their clinical data with me and helpful discussions. In addition, I am grateful to the members of the Frank Lab and fellow grad stu- dents and faculty at Brown. Specifically, Imri Sofer and Christopher Chatham have contributed in a major way through many thoughtful discussions and their friendship. I would like to thank my friends and family for their continuous support. Finally, I am grateful to my wife, Emiri, for moving across the Atlantic to join me, and for being the source of inspiration for this research. To her I dedicate these pages. iv Abstract The premise of the emerging field of computational psychiatry is to use models from computational cognitive neuroscience to gain deeper insights into mental illness. In this thesis my goal is to provide an overview of this endeavor and advance it by developing new software as well as quantitative methods. To demonstrate their use- fulness I will apply these methods to real-world data sets. A central theme will be the bridging of multiple levels of analysis of the brain ranging from neuroscience and cognition to behavior. In chapter 1 Idescribethecurrentcrisisinresearchandtreat- ment of mental illness and argue that computational psychiatry provides the tools to solve some long-standing issues that hindered progress in this area. I describe these tools by reviewing the current literature on computational psychiatry and demon- strate their usefulness on two real-world data sets. To provide a coherent scope, I will focus on response inhibition as it provides a rich literature in each of the di↵erent levels of analysis with clear links to psychopathology. In chapter 2 Ifirstestablish aneuronalbasisbypresentingabiologicallyplausibleneuralnetworkmodelofkey areas involved in response inhibition. Capturing the high-level computations of this fairly complex model requires more abstract cognitive process models. Towards this goal we developed software (chapter 3)toestimateadecisionmakingmodelinahier- archical Bayesian manner which improves parameter recovery in a simulation study. In chapter 4 I then bridge the neuronal and cognitive level by fitting a psychological process model to the simulated behavioral output of the neural network model under certain biological manipulations. By analyzing which biological manipulation is best v captured by changes in certain high-level computational parameters I start to link both levels of analysis. I then apply this same psychological process model to two data sets from selective response inhibition tasks administered to patients su↵ering from Huntington’s disease (chapter 5) and depression (chapter 6). Having identified neurobiological correlates of certain model parameters allows to then formulate the- ories not only about cognitive processes impacted by these disorders but also which neuronal mechanism are likely to be involved. In addition, I demonstrate that the description of subjects’ performance by computational model parameters can lead to better classification accuracy of disease state when compared to traditionally used summary statistics. vi Contents 1 Model-based cognitive neuroscience approaches to computational psychiatry: clustering and classification 1 1.1 Abstract .................................. 1 1.2 Motivation ................................. 2 1.2.1 Current challenges in psychiatry ................. 5 1.3 Potential Solutions ............................ 7 1.3.1 Research Domain Criteria Project (RDoC) and A Roadmap for Mental Health Research in Europe (ROAMER) ........ 8 1.3.2 Neurocognitive phenotyping ................... 9 1.3.3 Computational psychiatry .................... 11 1.4 Levels of Computational Psychiatry ................... 20 1.4.1 Level 1: Cognitive tasks ..................... 22 1.4.2 Level 2: Computational models ................. 24 1.4.3 Level 3: Parameter estimation .................. 30 1.4.4 Level 4: Supervised and unsupervised learning ......... 32 1.5 Example applications ........................... 34 1.5.1 Supervised and unsupervised learning of age .......... 34 1.5.2 Simulation experiment ...................... 39 1.5.3 Predicting brain state based on EEG .............. 40 1.6 Applications and challenges ....................... 44 1.7 Conclusions ................................ 46 vii 1.8 Acknowledgements ............................ 47 2 A computational model of inhibitory control in frontal cortex and basal ganglia 48 2.1 Abstract .................................. 48 2.2 Introduction ................................ 49 2.3 Neural Network Model .......................... 56 2.3.1 Selective Response Inhibition .................. 66 2.3.2 Global Response Inhibition .................... 80 2.4 Discussion ................................. 87 2.4.1 Selective Response Inhibition .................. 88 2.4.2 Global Response Inhibition .................... 92 2.4.3 Di↵erent forms of response inhibition .............. 94 2.4.4 Multiple mechanisms of response threshold regulation in fronto- basal-ganglia circuitry at di↵erent time scales ......... 95 2.4.5 Psychiatric disorders and di↵erential e↵ects of dopamine and norepinephrine .......................... 97 2.5 Limitations ................................ 98 2.5.1 Specificity of PFC regions and function ............. 98 2.5.2 Learning .............................. 99 2.6 Conclusions ................................ 99 2.7 Acknowledgments ............................. 100 3 HDDM: Hierarchical Bayesian estimation of the Drift-Di↵usion Model in Python 101 3.1 Abstract .................................. 101 3.2 Introduction ................................ 102 3.3 Methods .................................. 105 3.3.1 Drift Di↵usion Model ....................... 105 viii 3.3.2 Hierarchical Bayesian Estimation of the Drift-Di↵usion Model 108 3.4 Results ................................... 111 3.4.1 Loading data ........................... 111 3.4.2 Fitting a hierarchical model ................... 113 3.4.3 Fitting regression models ..................... 119 3.5 Simulations ................................ 121 3.5.1 Experiment 1 and 2 setup .................... 121 3.5.2 Experiment 3 setup ........................ 123 3.5.3 Results ............................... 123 3.6 Discussion ................................. 126 3.7 Acknowledgements ............................ 127 4 Bridging the gap: Relating biological and psychological models of the response inhibition 128 4.1 Introduction ................................ 128 4.2 Methods .................................. 129 4.3 Results ................................... 134 4.4 Discussion ................................. 135 4.4.1 Multiple mechanisms of decision threshold regulation in fronto- basal-ganglia circuitry at di↵erent time scales ......... 139 5 A computational cognitive biomarker for cognitive and motor con- trol deficits in Huntington’s Disease 142 5.1 Introduction ................................ 143 5.2 Methods .................................. 145 5.2.1 Distributional analysis ...................... 146 5.2.2 Computational modeling ..................... 147 5.2.3 Machine Learning ......................... 149 5.3 Results ................................... 150 ix 5.3.1 Behavior .............................. 150 5.3.2 Distributional analysis ...................... 151 5.3.3 Computational modeling ..................... 151 5.3.4 Machine Learning ......................... 156 5.4 Discussion ................................. 161 5.5 Acknowledgements ............................ 166 6 A Computational Analysis of Flanker Interference in Depression 167 6.1 Abstract .................................. 167 6.2 Introduction ................................ 168 6.3 Method .................................. 171 6.3.1 Participant recruitment, eligibility criteria, and payment ... 172 6.3.2 Questionnaires .......................... 173 6.3.3 Flanker task ............................ 173 6.3.4 Quality control .......................... 174 6.3.5 Analysis of flanker interference e↵ects on accuracy and RT