Mood Disorders: Brain Imaging and Therapeutic Implications

Half title
Title page
Imprints page
Contents
Contributors
Preface
Section 1 General
Chapter 1 Brain Imaging Methods in Mood Disorders
1.1 Introduction
1.2 Clinical Features
1.3 Etiology and Pathophysiology of Mood Disorders
1.4 Neuroimaging Techniques
1.5 Clinical Applications of Neuroimaging
1.6 Conclusion
References
Section 2 Anatomical Studies
Chapter 2 Neuroanatomical Findings in Unipolar Depression and the Role of the Hippocampus
2.1 Introduction
2.2 Structural Magnetic Resonance Imaging in Major Depressive Disorders
2.3 Postmortem Studies in Depression
2.4 Evidence of Structural Changes in Depression Following Prolonged Stress, and the Role of the Hippocampus and Hypothalamic–Pituitary–Adrenal Axis
2.5 Hippocampal Abnormalities: State versus Trait Marker for a Depressive Episode
2.6 Neuroanatomical Circuitry Involved in Major Depression
References
Chapter 3 Neuroanatomical Findings in Bipolar Disorder
3.1 Introduction
3.2 Case-Control Studies
3.2.1 Lateral Ventricles
3.2.2 Subcortical Structures
3.2.3 Cortical Regions
3.2.4 White Matter
3.2.5 Diffusion Tensor Imaging
3.2.6 Structural Network Findings
3.3 Bipolar Disorder Compared with Schizophrenia and Major Depressive Disorder
3.4 Sources of Heterogeneity
3.4.1 Psychotropic Medication
3.4.2 Demographic and Clinical Variables
3.4.3 Longitudinal
3.5 Conclusions
References
Chapter 4 Neuroimaging Biomarkers in Pediatric Mood Disorders
4.1 Aberrant Responses to Stress and Reward
Reward processing in pediatric BD.
Reward processing in pediatric MDD.
4.2 Maladaptive Developmental Trajectories
4.3 Implications of Neurobiological Markers for Treatment
4.4 Pediatric BD Treatment Implications
4.5 Pediatric MDD Treatment Implications
4.6 Conclusion
References
Section 3 Functional and Neurochemical Brain Studies
Chapter 5 Brain Imaging of Reward Dysfunction in Unipolar and Bipolar Disorders
5.1 Introduction
5.2 Neurobiology of Reward Processing and Its Components
5.3 Major Depressive Disorder
5.3.1 Neural Correlates of Reward Anticipation
5.3.2 Neural Correlates of Reward Consumption
5.3.3 Neural Correlates of Reward Learning
5.4 Is Reward Dysfunction a Potential Endophenotype of MDD?
5.5 Bipolar Disorder
5.5.1 Neural Correlates of Reward Anticipation
5.5.2 Neural Correlates of Reward Consumption
5.6 Heritability of Reward Dysfunction in BD
5.7 Studies Comparing MDD to BD
5.8 Future Directions: Transdiagnostic Mechanisms and Multimodal Imaging
5.9 Conclusions
References
Chapter 6 Resting-State Functional Connectivity in Unipolar Depression
6.1 Background
6.2 MRI Neurocircuitry Findings
6.2.1 Limbic-Cortical-Striatal-Pallidal-Thalamic Circuit
6.2.2 Functional Connectivity Findings Related to Suicide in Depression
6.3 Core Brain Networks in Depression
6.3.1 Default Mode Network
6.3.2 Central Executive Network
6.3.3 Salience Network
6.3.4 Between-Network Connectivity
6.4 Functional Connectomics in Depression
6.5 Conclusions
*Corresponding author
Acknowledgment
References
Chapter 7 Functional Connectome in Bipolar Disorder
7.1 Introduction
7.2 The Functional Connectome
7.2.1 Methods to Study the Functional Connectome in Brain Imaging Studies
Task-Based Functional Connectivity:
Resting-State Low-Frequency BOLD Fluctuations Correlation (Connectivity):
Seed-Based Functional Connectivity Analysis:
Independent Component Analysis (ICA):
Graph-Theory Analysis:
7.2.2 The Functional Connectome in Bipolar Disorder
7.2.2.1 Study Designs for Investigation of Functional Connectome in Bipolar Disorder
7.2.2.2 ROI Based Analysis
7.2.2.2a Cortico-Limbic Connectivity
Task-Related Cortico-Limbic Psychophysiological Interaction (PPI) Studies:
Resting-State Reference ROI-Based Cortico-Limbic Connectivity Studies:
7.2.2.2b Cortico-Cortical Connectivity
Task-Related Cortico-Cortical Psychophysiological Interaction (PPI) Studies:
Resting-State Reference ROI-Based Cortico-Cortical Functional Connectivity Studies:
7.2.2.2c Intrinsic Subcortical Connectivity
Resting-State Reference ROI-Based Subcortical Functional Connectivity Studies:
7.2.2.3 Independent Component Analysis Studies
7.2.2.4 Graph-Theory Network Properties Studies
7.2.2.5 Functional Connectome in Subjects At-Risk for Bipolar Disorder
Task-Related Psychophysiological Interaction (PPI) High-Risk Studies:
Resting-State Functional Connectivity High-Risk Studies:
Graph-Theory Analysis High-Risk Studies:
7.2.2.6 Treatment Effect on the Functional Connectome in BD
7.2.2.7 Psychotic Bipolar Disorder
7.3 Discussion
References
Chapter 8 Magnetic Resonance Spectroscopy Investigations of Bioenergy and Mitochondrial Function in Mood Disorders
Abbreviations
8.1 Introduction
8.2 Magnetic Resonance Spectroscopy and Brain Bioenergetics
8.3 Major Depressive Disorder
8.4 Bipolar Disorder
8.5 Conclusion
References
Chapter 9 Imaging Glutamatergic and GABAergic Abnormalities in Mood Disorders
9.1 Background
9.2 Glutamate
9.3 GABA
9.4 Magnetic Resonance Spectroscopy
9.5 Major Depressive Disorder and Glutamate
9.6 Bipolar Disorder and Glutamate
9.7 Major Depressive Disorder and GABA
9.8 Bipolar Disorder and GABA
9.9 Concluding Remarks and Clinical Relevance
References
Chapter 10 Neuroimaging Brain Inflammation in Mood Disorders
10.1 Introduction
10.2 Rationale for Neuroimaging Inflammation in Mood Disorders
10.3 Translocator Protein Imaging
10.4 Novel PET Imaging Probes of Neuroinflammation
10.5 Conclusions
References
Section 4 Novel Approaches in Brain Imaging
Chapter 11 Imaging Genetic and Epigenetic Markers in Mood Disorders
11.1 Genome-Wide Association Studies
11.2 Polygenic Risk Score
11.3 Candidate Gene Studies
11.4 Epigenetics
References
Chapter 12 fMRI Neurofeedback as Treatment for Depression
12.1 Introduction
12.2 The Neural Basis of Neurofeedback
12.3 fMRI-NF Neurofeedback Treatments for Depression
12.3.1 Mechanisms Shared across fMRI-NF Neurofeedback Treatments for Depression
12.3.1.1 Self-regulation
12.3.1.2 Reinforcement and Regulation of Neural States
12.3.2 Different fMRI-NF Neurofeedback Approaches in Depression
12.3.2.1 Emotion Regulation: Positive Affect
General Positive Affect
Saliency of Positive Affective Experiences
12.3.2.2 Emotion Regulation: Negative Affect
General Negative Affect
Saliency of Negative Affective Experiences
12.3.2.3 Connectivity Neurofeedback
12.4 Discussion
References
Chapter 13 Functional Near-Infrared Spectroscopy Studies in Mood Disorders
13.1 Introduction
13.2 Principle of Near-Infrared Spectroscopy
13.3 Application to Mood Disorders
13.3.1 Major Depressive Disorder versus Healthy Subjects
13.3.1.1 VFT, Letter Version
13.3.1.2 Other Tasks
13.3.2 Bipolar Disorder versus Healthy Subjects
13.3.3 Comparisons with Mood Disorders and Other Psychiatric Illnesses
13.4 Conclusions and Future Directions
References
Chapter 14 Electrophysiological Biomarkers for Mood Disorders
14.1 Introduction
14.1.1 Event-Related Potentials
14.1.2 P300
14.1.3 Diagnosis
14.1.4 Treatment Prediction
14.2 Error-Related Negativity
14.2.1 Diagnosis
14.2.2 Treatment Prediction
14.3 Loudness Dependence of the Auditory Evoked Potential
14.3.1 Diagnosis
14.3.2 Treatment Prediction
14.3.2.1 Quantitative EEG
14.4 Alpha Activity
14.4.1 Diagnosis
14.4.2 Treatment Prediction
14.5 Theta Activity
14.5.1 Diagnosis
14.5.2 Treatment Prediction
14.6 Gamma Activity
14.6.1 Diagnosis
14.6.2 Treatment Prediction
14.7 Limitations and Recommendations to the Field
References
Chapter 15 Magnetoencephalography Studies in Mood Disorders
15.1 Introduction to Magnetoencephalography
15.2 MEG in Mood Disorders
15.3 Sensory Evoked Fields
15.3.1 Motor and Somatosensory Evoked Fields
15.3.2 Auditory Evoked Fields
15.4 Emotional Paradigms
15.4.1 Evoked Responses to Affective Stimuli
15.4.2 Induced Oscillatory Responses to Affective Stimuli
15.4.3 Other Cognitive Tasks
15.5 Resting-State MEG
15.5.1 Spectral Power
15.5.2 Nonlinear Measures
15.5.3 Connectivity
15.6 MEG and Neuromodulation
15.7 Conclusions
Acknowledgments
References
Chapter 16 An Overview of Machine Learning Applications in Mood Disorders
16.1 Machine Learning: An Answer to Historic Challenges in Psychiatry?
16.2 Machine Learning Techniques
16.2.1 Supervised ML
16.2.2 Unsupervised Learning
16.2.3 Reinforcement Learning
16.2.4 Selection of Algorithm Training and Validation Samples
16.2.5 Feature and Data Dimensionality Reduction
16.2.6 Model Training and Parameter Optimization
16.3 Applications of Machine Learning Techniques to Neuroimaging and Clinical Data in Mood Disorders
16.3.1 Diagnostic Classification of Mood Disorders, Decoding Clinical Variables, Identification of Unique Disease Subtypes and Supporting Mechanistic Understanding
16.3.2 Prediction of Treatment Response
16.3.3 Prediction of Other Clinical Outcomes Such As Suicide, Medication Side Effects and Clinical Staging
16.4 Conclusion
16.4.1 Interpretability of Machine Learning Models and Variability in Implementations
16.4.2 Lack of Standards and Other Issues Around Ethics
References
Section 5 Therapeutic Applications of Neuroimaging in Mood Disorders
Chapter 17 Effects of Lithium on Brain Structure in Bipolar Disorder
17.1 Introduction
17.2 Magnetic Resonance Imaging Studies
17.3 Mechanisms of Brain Changes Associated with Lithium Treatment
17.4 Brain-Derived Neurotrophic Factor
17.5 Hippocampal Subregion Neuroplasticity
17.6 Summary and Future Directions
References
Chapter 18 Molecular Imaging of Dopamine and Antipsychotics in Bipolar Disorder
18.1 Introduction
18.2 Dopamine and Bipolar Disorder
18.3 The Use of Antipsychotics in Bipolar Disorder
18.3.1 Acute Treatment of Mania
18.3.2 Acute Treatment of Depression
18.3.3 Maintenance Treatment
18.4 Dopamine Synthesis and Metabolism
18.5 Molecular Imaging of the Dopamine System
18.5.1 Presynaptic System
18.5.2 Extra-Striatal Imaging of the Dopamine System
18.6 Molecular Imaging of the Dopamine System in Bipolar Disorder
18.6.1 Euthymic States
18.6.2 Bipolar Depression
18.6.3 Mania
18.6.4 Bipolar Psychosis
18.7 What is the Mechanism of Antipsychotic Response in Bipolar Disorder?
18.8 Future Directions
Acknowledgments
References
Chapter 19 Brain Imaging and the Mechanisms of Antidepressant Action
19.1 Introduction
19.2 Treatments in Context: A Short Account of the Neural Basis for Depression
19.3 An Impact of AD on the Brain
19.3.1 Impact of “Classical” Antidepressant Medications
19.3.2 Structural Effects of “Typical” AD Actions
19.3.3 Focus on Brain Connectivity
19.3.4 “Bottom-Up” or “Top-Down” Effect?
19.3.5 New Antidepressant Drugs: Ketamine
19.3.6 A Note on Ligand PET Studies
19.4 Understanding Finer-Grained Aspects of Antidepressant Action
19.4.1 The Role for Negative Bias Attenuation and Cognitive Neuropsychological Model
19.4.2 Different Drugs, Different Patterns of Neural Change?
19.4.3 Importance of Additional Factors for Antidepressant Action
19.4.4 Effect of a Single and Repeated Doses
19.5 Final Remarks
References
Chapter 20 Neuroimaging Studies of Effects of Psychotherapy in Depression
20.1 Introduction
20.2 Literature Search
20.3 Effects of Psychotherapy on the Brain in Depression
20.3.1 fMRI
20.3.2 PET and SPECT
20.3.3 MRS
20.4 Imaging as a Predictor of Psychotherapy Response
20.4.1 fMRI
20.4.2 PET
20.4.3 Structural MRI and EEG
20.5 Discussion
20.6 Final Conclusions
References
Index