Computational Neuroscience (Neuromethods, 199)

Description

This volume looks at the latest advancements in imaging neuroscience methods using magnetic resonance imaging (MRI) and electroencephalography (EEG) to study the healthy and diseased brain. The chapters in this book are organized into five parts. Parts One and Two cover an introduction to this field and the latest use of molecular models. Part Three explores neurophysiological methods for assessment, such as quantitative EEG and event-related potentials. Part Four discusses the advances and innovations made in computational anatomy, and Part Five addresses the challenges faced by researchers prior to the computational neuroscience to find wider translational applications in the field of psychiatry and mental health. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.  Cutting-edge and comprehensive, Computational Neuroscience is a valuable tool for researchers in the psychiatry and mental health fields who want to learn more about ways to incorporate computational approaches into utility and validity of clinical methods. Preface to the Series Preface Contents Contributors Part I: Introduction Chapter 1: Toward Methodology for Strategic Innovations in Translational and Computational Neuroscience in Psychiatry 1 Background 2 Current Advancements 2.1 Future Research Goals 2.2 Expected Results 3 Impact References Part II: Molecular Methods Chapter 2: Molecular Methods in Neuroscience and Psychiatry 1 Introduction 2 Methods 3 Results and Discussions 3.1 Methods in Neurotranscriptomics 3.2 Methods in Neuroproteomics 3.3 Methods in Epigenetics 3.4 Flow Cytometry in Psychiatry and Neuroscience 3.5 ELISpot/FluoroSpot in Psychiatry and Neuroscience 4 Conclusions References Chapter 3: Toward the Use of Research and Diagnostic Algorithmic Rules to Assess the Recurrence of Illness and Major Dysmood D... 1 Mood Disorder Concepts: The Ultimate Chaos 2 Diagnosis of Mood Disorders: The Ultimate Chaos 3 Lack of a Correct Model Prevents Targeted Research 4 Machine Learning Models 5 RADAR Scores and Plots 6 Why the Diagnosis ``Bipolar Disorder´´ Is Useless 6.1 Patients with BP1 and BP2 May Be Classified as SMDM or MDMD 6.2 Depressive and Manic Episodes Are Manifestations of ROI 6.3 The Diagnoses of MDD, MDE, BP1, and BP2 Are Irrelevant in Our Precision Models 6.4 No Model Differences Between Unipolar and Bipolar Disorders 7 Conclusions References Part III: Neurophysiological Methods Chapter 4: The Concept of Event-Related Oscillations: A Spotlight on Extended Applications Abbreviations 1 The Concept of Event-Related Oscillations 1.1 Conceptual Framework 1.1.1 Event-Related Potentials 1.1.2 Event-Related EEG Oscillations 1.2 Advantages 1.2.1 A Full Characterization of Event-Related EEG Signals 1.2.2 Evaluation of Parallel Processes in the Brain 1.2.3 A Physiological Approach to the Event-Related EEG Activity 2 Methodology 2.1 Analysis in the Frequency Domain 2.2 Analysis in the Time-Frequency Domain 2.2.1 Phase-Locked Power 2.2.2 Total Power 2.2.3 Temporal Phase Locking Single-Sweep Wave Identification Method Phase-Locking Factor 2.2.4 Event-Related Spatial Synchronization: Spatial Phase Locking Phase-Locking Value Phase-Lag Index 3 Extended Applications 3.1 Internal Information Processing 3.1.1 Response-Related Potentials 3.1.2 Coupling Between Slow Oscillations and Sleep Spindles 3.1.3 Additional Internal Potentials: A Future Focus of Research 3.2 Event-Related Frequency Tuning 3.3 Event-Related Spatial Synchronization 3.4 Multi-Second Behavioral Patterns 4 Concluding Remarks References Chapter 5: Quantitative EEG Analysis: Introduction and Basic Principles 1 Introduction References Chapter 6: QEEG and ERP Biomarkers of Psychotic and Mood Disorders and Their Treatment Response 1 The Perspective of Clinical Utility of Mismatch Negativity and P300 Event-Related Potentials in Psychotic Disorders 1.1 MMN 1.2 P300 2 Quantitative EEG Biomarkers of Depression and Antidepressant Treatment Response References Chapter 7: Quantitative EEG in Patients with Schizophrenia 1 Introduction References Part IV: Neuroimaging Methods Chapter 8: Computational Anatomy Going Beyond Brain Morphometry 1 Introduction 2 Historical Overview 3 Computational Anatomy in Basic and Clinical Neuroscience 4 Limitations of Computational Anatomy Using T1-Weighted Data 5 Improved Brain Tissue Classification Using qMRI 6 ``In Vivo Histology´´ Using qMRI 7 Current Limitations of qMRI 8 Outlook References Chapter 9: Nonlinear Methods for the Investigation of Psychotic Disorders 1 Introduction 2 How to Evaluate Nonlinear Dynamical Systems? 3 Methods 4 Use Cases 5 Summary and Outlook References Chapter 10: Carving Out the Path to Computational Biomarkers for Mental Disorders 1 Introduction 1.1 The Complexity of Understanding Brain Function and Dysfunction 1.2 The Role of Emotions in Anxiety and Other Mental Disorders 1.3 The Role of the Amygdala in Emotions 1.4 Amygdala Activation and Connectivity in Anxiety and Other Mental Disorders 1.5 Structural and Functional Alterations in the Amygdala as a Possible Differential Diagnostic Biomarker for Mental Disorders 1.6 Real World Challenges for Amygdala-Based Biomarkers 2 Materials 2.1 Magnetic Resonance Imaging Hardware 2.2 Computing Hardware 3 Methods 3.1 Experimental Design: Amygdala Function 3.2 Experimental Design: Amygdala Anatomy 3.3 Experimental Design: Connectivity and Amygdala Regulation 3.4 Functional MRI of the Amygdala 3.5 Ultra-High Field Functional MRI of the Amygdala 4 Conclusion References Chapter 11: Neuroimaging Methods Investigating Anterior Insula Alterations in Schizophrenia and Mood Disorders 1 Introduction 2 Structural Changes 3 Functional Alterations 4 Impaired Connectivity 5 Conclusion References Chapter 12: Magnetic Resonance Spectroscopy 1 Introduction 1.1 Magnetic Resonance Spectroscopy: Principles 1.2 Clinical Applications of MRS 1.3 MRS Applications in Neurology 1.4 MRS Applications in Psychiatry 1.5 Functional MRS 2 Materials 3 Methods 3.1 Data Acquisition 3.1.1 Single Voxel Spectroscopy 3.1.2 MRS Imaging 3.1.3 Water and Lipid Suppression 3.2 Data Processing 4 Notes 4.1 Understanding Data Quality 4.2 Artifacts 4.3 Long and Short TE References Chapter 13: The Effect of Exogenous and Endogenous Parameters on Group Resting-State Effective Connectivity and BOLD Signal 1 Introduction 2 Methods 2.1 Participants 2.2 BETULA Data Collection 2.3 Data Analysis 2.3.1 Pre-processing 2.3.2 ROI Selection 2.3.3 DCM Analysis 3 Results 3.1 Effective Connectivity 3.2 Parameters Defining the BOLD Signal 4 Discussions 5 Conclusions References Chapter 14: Utility of Computational Approaches for Precision Psychiatry: Applications to Substance Use Disorders 1 Introduction 2 Theory-Driven Approaches: Computational Modeling and Computational Phenotyping 2.1 Joint Modeling 3 Hybrid Approaches/Adaptive Design Optimization 4 Data-Driven Approaches/Machine Learning 5 Summary and Conclusion References Part V: Integrative Computational Neuroscience Chapter 15: Multimodal Integration in Psychiatry: Clinical Potential and Challenges 1 Introduction 2 Materials 3 Methods 3.1 Multimodality of Magnetic Resonance Techniques 3.2 Multimodal Magnetic Resonance in the Study of Major Psychoses 3.3 Functional Neuroimaging and Neurophysiologic Techniques 3.3.1 Magnetic Resonance Imaging of the BOLD Effect 3.4 MRI Techniques Sensitive to Perfusion and Oxidative Metabolism 3.5 Optical Neuroimaging Techniques 3.6 Positron Emission Tomography 3.7 Electroencephalography 4 Conclusions References Chapter 16: Premises of Computational Neuroscience: Machine Learning Tools and Multivariate Analyses 1 Introduction 2 Guide to the Methodology 2.1 Overview 2.2 Mathematical Formulations 2.3 Benefits and Limitations of Using Multivariate Methods for Mental Health 3 Examples of Application of Multivariate Methods in Mental Health 3.1 Multivariate Methods Applied to the Classification of Schizophrenia 3.1.1 Objective 3.1.2 Data Used 3.1.3 Method Used 3.1.4 Results 3.2 Individual- and Group-Level Brain Signatures of Schizophrenia, Major Depressive, and Bipolar Disorders 3.2.1 Objective 3.2.2 Data 3.2.3 Method 3.2.4 Results 3.3 Multimodel Brain Signature with Task-fMRI, Resting State, and Morphometry in Schizophrenia and Major Depressive Disorder 3.3.1 Objective 3.3.2 Data 3.3.3 Methods 3.3.4 Results 4 Code and Toolbox Availability 5 Conclusion References Index