Format:
1 Online-Ressource (334 pages)
ISBN:
9780128098264
Content:
Front Cover -- COMPUTATIONAL PSYCHIATRY -- COMPUTATIONAL PSYCHIATRY: MATHEMATICAL MODELING OF MENTAL ILLNESS -- Copyright -- Contents -- Contributors -- Preface -- Meeting Emerging Challengesand Opportunities in PsychiatryThrough ComputationalNeuroscience -- PATHS TOWARD MECHANISTIC DISCOVERY IN PSYCHIATRY -- TACKLING COMPLEXITY OF MECHANISM VIA COMPUTATIONAL NEUROSCIENCE -- THE CURRENT STATE OF COMPUTATIONAL PSYCHIATRY -- The Potential Impact of Computational Psychiatry and What Is Needed to Get There -- Integration Across Levels of Analysis -- Computational Phenotyping and Biomarker Refinement -- Modeling Treatments -- WHAT COMPUTATIONAL PSYCHIATRY NEEDS TO SUCCEED? -- Common Language -- Generating Multilevel Data -- Mapping Categorical Versus Continuous Alterations: RDOC Versus DSM -- Conceptually Separate "Big Data Analytics" From "Theory" -- Sharing, Standardization, and Reproducibility -- Ground-Truth Datasets for Benchmarking of Models -- Infrastructure -- Not Losing Track of Time -- Linking Theoretical Approaches -- Training and Development of the Scientific Workforce -- Facilitating Dialogue Between Computational and Experimental Neuroscience -- CONCLUDING REMARKS -- References -- I - APPLYING CIRCUIT MODELING TO UNDERSTAND PSYCHIATRIC SYMPTOMS -- 1 - Cortical Circuit Models in Psychiatry: Linking Disrupted Excitation-Inhibition Balance to Cognitive Deficits As ... -- 1.1 INTRODUCTION -- 1.2 ROLES FOR BIOPHYSICALLY BASED NEURAL CIRCUIT MODELING IN COMPUTATIONAL PSYCHIATRY -- 1.3 LINKING PROPOSITIONS FOR COGNITIVE PROCESSES -- 1.4 ATTRACTOR NETWORK MODELS FOR CORE COGNITIVE COMPUTATIONS IN RECURRENT CORTICAL CIRCUITS -- 1.5 CIRCUIT MODELS OF COGNITIVE DEFICITS FROM ALTERED EXCITATION-INHIBITION BALANCE -- 1.5.1 Working Memory -- 1.5.2 Decision Making -- 1.6 CRITICAL ROLE OF EXCITATION-INHIBITION BALANCE IN COGNITIVE FUNCTION
Content:
1.7 FUTURE DIRECTIONS IN NEURAL CIRCUIT MODELING OF COGNITIVE FUNCTION -- 1.7.1 Integrating Cognitive Function With Neurophysiological Biomarkers -- 1.7.2 Incorporating Further Neurobiological Detail -- 1.7.3 Informing Task Designs -- 1.7.4 Studying Compensations and Treatments -- Acknowledgments -- References -- 2 - Serotonergic Modulation of Cognition in Prefrontal Cortical Circuits in Major Depression -- 2.1 METHODS -- 2.2 RESULTS -- 2.3 DISCUSSION -- Acknowledgments -- References -- 3 - Dopaminergic Neurons in the Ventral Tegmental Area and Their Dysregulation in Nicotine Addiction -- 3.1 NICOTINE, DOPAMINE, AND ADDICTION -- 3.2 MODELING RECEPTOR KINETICS -- 3.2.1 Ligand-Receptor Interaction -- 3.2.1.1 A Two-Gate Receptor Model -- 3.2.1.2 Steady-State Receptor Current -- 3.2.1.3 Temporal Dynamics of the Receptor Current -- 3.2.2 Competition and Cooperation Between Ligands, or Between Ligand and the Endogenous Transmitter -- 3.2.2.1 Competitive Inhibition -- 3.2.2.2 Coagonism -- 3.2.2.3 Positive Allosteric Modulation -- 3.2.3 Miscellaneous and Secondary Receptor Effects -- 3.3 CIRCUIT MODELS OF THE VENTRAL TEGMENTAL AREA -- 3.3.1 Reorganization of the Ventral Tegmental Area in Addiction -- 3.3.2 Circuit Simulations of the Normal and Reorganized Ventral Tegmental Area -- 3.4 MODELING TONIC VERSUS PHASIC DOPAMINE RELEASE -- 3.4.1 Modeling the Firing Pattern of Dopamine Neurons -- 3.4.2 Effects of Nicotine on the Dopamine Cell's Spike Pattern -- 3.5 SUMMARY -- APPENDIX A: THE DOPAMINE NEURON MODEL -- References -- II - MODELING NEURAL SYSTEM DISRUPTIONS IN PSYCHIATRIC ILLNESS -- 4 - Computational Models of Dysconnectivity in Large-Scale Resting-State Networks -- 4.1 INTRODUCTION -- 4.1.1 The Study of Large-Scale Brain Connectivity -- 4.2 RESTING-STATE FUNCTIONAL CONNECTIVITY AND NETWORKS IN FUNCTIONAL MAGNETIC RESONANCE IMAGING
Content:
4.3 DYNAMIC FUNCTIONAL CONNECTIVITY -- 4.4 MEASURING STRUCTURAL CONNECTIVITY -- 4.5 EFFECTIVE CONNECTIVITY -- 4.6 TOPOLOGICAL ANALYSIS OF THE NETWORKS -- 4.7 COMPARING CONNECTIVITY AMONG GROUPS -- 4.7.1 Clinical Applications of Large-Scale Resting State Connectivity -- 4.8 MODELING THE LARGE-SCALE BRAIN ACTIVITY-I: LINKING STRUCTURE AND FUNCTION -- 4.9 MODELING THE LARGE-SCALE BRAIN ACTIVITY-II: ADDING DYNAMICS INTO THE EQUATION -- 4.10 DISCUSSION -- References -- 5 - Dynamic Causal Modeling and Its Application to Psychiatric Disorders -- 5.1 INTRODUCTION TO DYNAMIC CAUSAL MODELING -- 5.1.1 Dynamic Causal Models for Functional Magnetic Resonance Imaging -- 5.1.2 Dynamic Causal Models for Electrophysiological Data -- 5.1.3 Model Comparison -- 5.1.3.1 Model Family Comparison -- 5.1.4 Model Parameter Estimates: Physiological and Clinical Interpretations -- 5.1.4.1 Posterior Parameter Estimates -- 5.1.4.2 Model Averaging -- 5.1.4.3 Generative Embedding -- 5.1.5 Other Variants of Dynamic Causal Modeling -- 5.2 APPLICATION OF DYNAMIC CAUSAL MODELING IN PSYCHIATRY -- 5.2.1 Using Dynamic Causal Modeling to Understand Mechanism of Behavioral/Cognitive Dysfunction -- 5.2.2 Using Dynamic Causal Modeling to Investigate Synaptic Dysfunction -- 5.2.3 Using Dynamic Causal Modeling to Dissect Spectrum Disorders -- 5.2.4 Current Dynamic Causal Modeling Limitations -- 5.3 OUTLOOK -- Acknowledgments -- References -- 6 - Systems Level Modeling of Cognitive Control in Psychiatric Disorders: A Focus on Schizophrenia -- 6.1 INTRODUCTION -- 6.2 MECHANISMS OF CONTROL: PROACTIVE AND REACTIVE -- 6.2.1 Proactive Versus Reactive Control Deficits in Schizophrenia -- 6.2.2 Computational Models of Proactive and Reactive Control -- 6.2.2.1 Connectionist Modeling of Proactive Control in Schizophrenia: Guided Activation Framework
Content:
6.2.2.1.1 Using Proactive Control Models to Make Predictions About Dorsolateral Prefrontal Cortex Activity -- 6.2.2.2 Reactive Control-When to Engage or Upregulate Control -- 6.2.2.2.1 Performance Monitoring and Reactive Control in Schizophrenia -- 6.2.2.3 Relationships Between Proactive Control and Reactive Control in Schizophrenia -- 6.3 UPDATING CONTROL REPRESENTATIONS-DOPAMINE, THE STRIATUM AND A GATING MECHANISM -- 6.3.1 Dopamine and Gating in Schizophrenia -- 6.4 COGNITIVE CONTROL, VALUE, AND EFFORT ALLOCATION -- 6.4.1 Cognitive Control, Utility, and Exploitation Versus Exploration -- 6.4.2 Model-Based Learning and a Decision-Making as a Form of Cognitive Control -- 6.4.3 Cognitive Control Impairments as a Core Feature of Psychopathology -- 6.5 SUMMARY AND FUTURE DIRECTIONS -- References -- 7 - Bayesian Inference, Predictive Coding, and Computational Models of Psychosis -- 7.1 HIERARCHICAL MODELS AND PREDICTIVE CODING -- 7.2 PSYCHOSIS, SYNAPTIC GAIN, AND PRECISION -- 7.3 COMPUTATIONALLY MODELING THE FORMATION OF DELUSIONS -- 7.4 MODELING THE MAINTENANCE OF DELUSIONS -- 7.5 CONCLUSIONS AND FUTURE DIRECTIONS -- References -- III - CHARACTERIZING COMPLEX PSYCHIATRIC SYMPTOMS VIA MATHEMATICAL MODELS -- 8 - A Case Study in Computational Psychiatry: Addiction as Failure Modes of the Decision-Making System -- 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 -- 9 - Modeling Negative Symptoms in Schizophrenia -- 9.1 INTRODUCTION: NEGATIVE SYMPTOMS IN SCHIZOPHRENIA -- 9.2 DOPAMINE SYSTEMS AND PREDICTION ERRORS -- 9.3 MODELING IN REWARD-RELATED DECISION TASKS -- 9.4 PROBABILISTIC STIMULUS SELECTION-COMBINED ACTOR-CRITIC/Q-LEARNING -- 9.4.1 Rationale -- 9.4.2 Q-Learning
Content:
9.4.3 Actor-Critic Model -- 9.4.4 Combined Actor-Critic/Q-Learning -- 9.4.5 Findings -- 9.5 TIME CONFLICT-TEMPORAL UTILITY INTEGRATION TASK -- 9.5.1 Rationale -- 9.5.2 Time Conflict Model -- 9.5.3 Findings -- 9.6 PAVLOVIAN BIAS-EXTENDED Q-LEARNING -- 9.6.1 Rationale -- 9.6.2 Pavlovian Bias Model -- 9.6.3 Findings -- 9.7 DIRECT ADDITION OF WORKING MEMORY TO REINFORCEMENT LEARNING MODELS -- 9.7.1 Rationale -- 9.7.2 Reinforcement Learning and Working Memory Model -- 9.7.3 Findings -- 9.8 SUMMARY -- 9.9 CONCLUSION -- References -- 10 - Bayesian Approaches to Learning and Decision-Making -- 10.1 INTRODUCTION -- 10.2 MARKOV DECISION PROBLEMS -- 10.2.1 Bellman Equation -- 10.2.2 Solving the Bellman Equation -- 10.2.2.1 Model-Free Temporal Difference Prediction Error Learning -- 10.2.2.2 Phasic Dopaminergic Signals -- 10.2.3 Policy Updates -- 10.3 MODELING DATA -- 10.3.1 General Considerations -- 10.3.2 A Toy Example -- 10.3.3 Generating Data -- 10.3.4 Fitting Models -- 10.3.5 Model Comparison -- 10.3.6 Group Studies -- 10.4 DISSECTING COMPONENTS OF DECISION-MAKING -- 10.4.1 Reward Learning -- 10.4.2 Pavlovian Influences -- 10.4.3 Model-Based and Model-Free Decision-Making -- 10.4.4 Complex Planning -- 10.5 DISCUSSION -- References -- 11 - Computational Phenotypes Revealed by Interactive Economic Games -- 11.1 INTRODUCTION -- 11.2 REINFORCEMENT LEARNING SYSTEMS AND THE VALUATION OF STATES AND ACTIONS -- 11.3 REACHING TOWARD HUMANS -- 11.4 COMPUTATIONAL PROBES OF PSYCHOPATHOLOGY USING HUMAN SOCIAL EXCHANGE: HUMAN BIOSENSOR APPROACHES -- 11.5 EPILOGUE: APPROACH AND AVOIDANCE IS NOT RICH ENOUGH -- References -- Further Reading -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- V -- W -- Z -- Back cover
Additional Edition:
9780128098257
Additional Edition:
Print version Anticevic, Alan Computational Psychiatry : Mathematical Modeling of Mental Illness Saint Louis : Elsevier Science,c2017 9780128098257
Language:
English
URL:
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