Personalized Psychiatry

Name: Personalized Psychiatry
Brand: Academic Press
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Bernhard Baune(Herausgeber*in)

Academic Press

Erschienen am 16. Oktober 2019

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Front Cover
Personalized Psychiatry
Copyright
Contents
Contributors
Preface
Chapter 1: What is personalized psychiatry and why is it necessary?
Chapter 2: The modeling of trajectories in psychotic illness
1. Introduction
2. Sample trajectories (between-subject analyses)
2.1. Simple Bayesian trajectories
3. Subgroup and individual trajectories (between- and within-subject analyses)
3.1. Mixed modeling
3.2. Structural equation modeling
4. Discussion
4.1. Future designs in trajectory research
4.2. Conclusions
References
Chapter 3: Mood trajectories as a basis for personalized psychiatry in young people
1. Introduction
2. Three types of studies investigate depressive symptom trajectories in young people
3. The shape and the number of depressive symptom trajectories in young people
3.1. The shape of depressive symptom trajectories in young people
3.2. The number of depressive symptom trajectories in young people
4. Risk factors for specific mood symptom trajectory membership
4.1. Gender
4.2. Physical developmental factors
4.3. Parental history of mental disorders and other parental factors
4.4. Interpersonal relationship factors
4.5. Physical health factors
4.6. Socioeconomic factors
4.7. Psychosocial stressors in childhood
4.8. Ethnic factors
4.9. Psychological factors
4.10. Lifestyle factors
5. The contribution of specific and predefined risk factors to depressive symptom trajectories
5.1. Gender
5.2. Ethnicity and minority group membership
5.3. Physical health
5.4. Genetic factors
5.5. Psychological factors
6. Concurrent trajectories of depressive symptoms and other longitudinally measured factors
6.1. Concurrent trajectories of mood symptoms and trauma/life events/stress
6.2. Concurrent trajectories of mood and delinquency/oppositional defiance
6.3. Concurrent trajectories of depressive symptoms and body mass index
6.4. Concurrent trajectories of depressive symptoms and substance use
7. Summary of findings on depressive symptom trajectories in young people
8. Implications for clinical practice and research
9. Limitations
10. Conclusions
References
Further reading
Chapter 4: Transdiagnostic early intervention, prevention, and prediction in psychiatry
1. Introduction
2. Emergence and progression of mental disorders
3. Transdiagnostic approaches to diagnosis and classification
3.1. Clinical staging
4. Prediction of mental disorder onset and progression
4.1. Dynamic prediction techniques
4.1.1. Network theory
4.1.2. Dynamical systems theory
4.1.3. Joint modeling
5. Transdiagnostic early intervention and clinical services
5.1. Transdiagnostic, stage-based care
5.2. Applying a transdiagnostic clinical staging model within youth mental health services
6. Conclusion
References
Chapter 5: Early intervention, prevention, and prediction in mood disorders: Tracking multidimensional outcomes in young ...
1. Developing an early intervention mood disorders service
2. Characteristics of the Brain and Mind Center youth cohort
3. Data collection process
4. Demographic, symptom, and social and occupational functioning of the brain and mind youth mood disorders cohort
5. Developing a multidimensional outcome framework for young people with emerging mood disorders
6. Illness type, clinical stage, and lifetime trajectory
6.1. Illness type
6.2. Clinical stage
6.3. Lifetime trajectory
7. Testing the robustness and clinical utility of a multidimensional outcomes framework
8. Designing personalized treatments based on illness type and clinical stage
9. Designing health services to respond to early mood disorders: Opportunities for early intervention and secondary preve ...
10. Testing novel treatments
11. Conclusions
References
Chapter 6: Consumer participation in personalized psychiatry
1. Introduction
2. Consumer participation in research
2.1. Sociodemographic considerations
2.2. Citizen science as a model
2.3. Patient-Centered Outcomes Research Institute (PCORI)
2.4. Precision Medicine Initiative
2.5. Online patient communities as clinical and research tools
2.5.1. The example of PatientsLikeMe
3. Consumer participation in novel solution development
4. Consumer participation in clinical care
4.1. Shared decision making (SDM)
5. Consumer participation in policy
5.1. Reimbursement
6. Discussion
6.1. Future roles of online patient communities as clinical and research tools
6.2. Future roles of design thinking in novel solution development and shared decision making in clinical care
7. Conclusion
References
Chapter 7: Experimental validation of psychopathology in personalized psychiatry
1. Introduction
2. Psychopathology of anxiety disorders
2.1. Experimental validation of fear and anxiety
2.1.1. A transdiagnostic dimensional model of defensive behaviors
2.2. Empirical evaluation of the model in humans
2.2.1. Preencounter defense: Generalized hypervigilance
2.2.2. Postencounter defense: Defensive freezing
2.2.3. Circa-strike defense: Initiation of action
3. Neural network activation
4. Avoidance behavior as an index of functional impairment
5. Clinical implications: Anxiety disorders
5.1. Toward personalized treatment: Genetic and behavioral data
References
Further reading
Chapter 8: Deep brain stimulation for major depression: A prototype of a personalized treatment in psychiatry
1. The need for personalized treatment in psychiatry
2. Deep brain stimulation
2.1. Targets for depression
2.2. Antidepressant efficacy
2.3. DBS for treatment-resistant depression-State of the art
2.4. Stimulating with high spatial precision
2.4.1. How important is the high spatial precision?
2.5. From stimulating targets to modulating networks
2.6. Relating clinical symptom clusters to dysfunctional brain networks
2.7. Is there a target-Specific effect for subtypes of depression?
3. Current research strategies for the individualized treatment of depression
4. Summary and outlook
References
Chapter 9: The Psychiatric Genomics Consortium: History, development, and the future
1. Background
1.1. Heritability in psychiatric disorders
1.2. Linkage and candidate gene studies
1.3. Polygenicity
1.4. High-throughput assays
2. History, development, and contributions of the Psychiatric Genomics Consortium
2.1. History and aims
2.2. Contributions
2.2.1. General contributions
2.2.2. Schizophrenia
2.2.3. Bipolar disorder
2.2.4. Autism
2.2.5. Attention-deficit/hyperactivity disorder
2.2.6. Major depressive disorder
2.2.7. Anorexia nervosa
2.2.8. Other disorders
2.2.9. Rare variants
2.2.10. Cross-disorder GWAS
2.2.11. Genetic risk scores
2.2.12. Genetic correlations
3. What does the future hold?
4. Conclusions
References
Chapter 10: Statistical genetic concepts in psychiatric genomics
1. Genetic studies
1.1. Genetic linkage studies
1.2. Genetic association studies
1.3. Genome-wide association studies
1.4. CNV analysis
1.5. Family-based association studies
1.6. Twin studies
2. Genetic architecture
2.1. Population stratification
2.2. Imputation of missing genotypes
2.3. Heritability, genetic correlation, and polygenic risk scores
3. Gene-environment interactions
4. Gene expression and DNA methylation analysis
5. Gene enrichment and network analysis
6. Conclusions and future directions
References
Further reading
Chapter 11: Opportunities and challenges of machine learning approaches for biomarker signature identification in psychiatry
1. Introduction
2. Opportunities
2.1. Knowledge incorporation via regularization
2.1.1. Penalization of feature value
2.1.2. Penalization of the difference between features
2.2. Knowledge incorporation via Bayesian methods
2.2.1. Bayesian statistical modeling
2.2.2. Bayesian supervised learning
2.2.3. Bayesian networks
2.3. Knowledge incorporation via Kernel methods
2.3.1. Customized kernels to incorporate biological network information
2.3.2. Feature selection using nonlinear kernels
3. Challenges
3.1. Imputation approaches based on correlation structure
3.1.1. Global approach
3.1.2. Local approach
3.1.3. Hybrid approach
3.2. Imputation approach based on additional knowledge
4. Conclusion
Acknowledgment
References
Further reading
Chapter 12: Personalized psychiatry with human iPSCs and neuronal reprogramming
1. Introduction
1.1. Psychiatry needs precision medicine
1.2. Limitations of existing (non-iPSC) models
2. iPSC-derived neurons for personalized psychiatry: Opportunities and challenges
2.1. Genetics, epigenetics, and iPSCs in psychiatric disease
2.1.1. iPSC technology, step 1: Reprogramming of patient-derived somatic cells to pluripotent states (iPSCs)
2.1.2. iPSC technology, step 2: Generating neurons relevant for psychiatric disorders from iPSCs
2.2. Applying iPSCs to personalized psychiatry
2.2.1. Correlating neuronal phenotypes with clinical phenotypes
2.2.2. Personalized investigations of the molecular neurobiology of psychiatric therapies
2.2.3. Personalized drug screening with patient-derived neurons
2.2.4. iPSCs as cell replacement therapies in psychiatry
2.3. Challenges for optimal use of neuronal reprogramming in personalized psychiatry
2.3.1. Time to generate neurons
2.3.2. The neurophysiological microenvironment in vitro
2.3.3. Optimizing reproducibility and reducing variability
2.3.4. The number of patient cell lines
2.3.5. Scalability
3. Using iPSCs for psychiatric disease modeling
3.1. iPSCs in bipolar disorder
3.2. iPSCs in depression and anxiety disorders
3.3. iPSCs in schizophrenia
3.4. iPSCs in autism spectrum disorder
4. Conclusions and future directions
References
Further reading
Chapter 13: Genetics of alcohol use disorder
1. Introduction
2. Twin studies
3. Linkage studies
3.1. Candidate gene association studies
3.2. Genome-wide association studies
3.3. GCTA/GREML methods
3.4. Whole genome sequencing
4. Conclusions
References
Chapter 14: Genomics of autism spectrum disorders
1. Definition of the clinical phenotype
1.1. Background and emerging diagnosis
1.2. Incidence
1.3. Cost of autism spectrum disorders
1.4. Current state of personalized care
2. Genetic causes of autism spectrum disorders
2.1. Syndromic ASD
2.2. Nonsyndromic
3. Diagnostic work-up for nonsyndromic autism spectrum disorders
4. Pathway analysis and molecular networks
5. Genetic counseling of autism spectrum disorders
5.1. Navigating the diagnostic work-up
5.2. Managing the patient and helping the family cope
6. Summary
References
Further reading
Chapter 15: Genomics of schizophrenia
1. Introduction
2. Schizophrenia heritability
3. An emerging genetic architecture of schizophrenia
4. Genome-wide association studies
5. Rare structural risk variants
6. Genome-sequencing studies
7. From gene discovery to etiology
8. Genetic architecture
9. Diagnostics
10. Localizing risk genes
11. Molecular mechanisms and pathways
12. Drug discovery
13. Personalized treatment
14. Conclusion
References
Chapter 16: Genomics of major depressive disorder
1. What is the phenotype?
2. Genetic epidemiology
3. Do proband characteristics predict familial risk?
4. Relationship of MDD to bipolar disorder and schizophrenia: Epidemiology
5. Childhood trauma and other stressful events
6. Molecular genetic methods
7. GWAS of depression
7.1. Large-scale GWAS analyses
7.2. What can we learn from the results so far?
7.3. Common-SNP heritability and genetic correlations among datasets
7.4. Genetic pathway findings
7.5. Single-locus associations
7.6. Genetic correlation with psychiatric and nonpsychiatric traits
8. Large-scale studies of gene expression
9. Studies of candidate genes and polymorphisms
9.1. 5-HTTLPR genotype and the risk of depressive episodes following stress
10. Pharmacogenomic prediction of antidepressant medication response
11. Conclusions and future directions
Disclosure Statement
References
Chapter 17: Personalized mental health: Artificial intelligence technologies for treatment response prediction in anxiety ...
1. Introduction
1.1. Interindividual variability in normal to pathological forms of fear and anxiety
1.2. What is a predictive biomarker?
1.3. Targeting nonrespondent patients by personalizing treatments?
1.4. Novel methods for ``mental health predictomics´´
1.5. Aims and contents of this chapter
2. Neurobiological markers of treatment response: Available evidence
2.1. (Epi)genetic markers
2.2. Neuroimaging markers
2.3. Single-case predictions
3. Toward personalized clinical decision support systems: Current status quo and future challenges
3.1. Model construction and validation: The relevance of real-life, heterogeneous patient samples
3.2. Novel approaches and technologies provided by machine learning and artificial intelligence
3.3. Sharing pretrained models rather than original data
3.4. Legal issues, data privacy, security, and ownership associated with predictive analytics
3.5. Turning robust, validated biomarker models into clinical decision support systems
4. Conclusions and outlook
References
Chapter 18: The genetic architecture of bipolar disorder: Entering the road of discoveries
1. Introduction
2. Family studies
3. Twin and adoption studies
4. Molecular genetics
4.1. Linkage studies
4.2. Association studies
5. Genome-wide association studies
6. GWAS in BD
7. Genetic architecture of BD
7.1. Recent GWAS of BD
7.2. Genetic overlap
7.3. Novel polygenic analytical approaches
8. From genetic loci to disease mechanisms
9. Contribution of rare variants
10. Clinical implications of genetic knowledge
11. Conclusion
Acknowledgments
Competing Financial Interests
References
Chapter 19: Genomics of borderline personality disorder
1. Clinical aspects of borderline personality disorder
2. Genetic studies
2.1. Formal genetic studies
2.2. Molecular genetic studies
2.2.1. Candidate genes
2.2.2. Genome-wide approaches
2.3. Epigenetic factors
3. Summary
4. Future directions
References
Chapter 20: Genetics of obsessive-compulsive disorder and Tourette disorder
1. Introduction
1.1. Heritability and familiality of OCD and TD
1.2. Candidate gene studies in OCD and TD
1.3. Genome-wide association studies in OCD and TD
1.4. Copy number variation in OCD and TD
1.5. Whole exome sequencing in OCD and TD
1.6. Pharmacogenetics of antidepressants in OCD and TD
1.7. Imaging genetics of OCD and TD
1.8. Epigenetics of OCD and TD
1.9. Genexenvironment of OCD and TD
2. Conclusion and future perspective
Acknowledgments
References
Chapter 21: Genetics and pharmacogenetics of attention deficit hyperactivity disorder in childhood and adulthood
1. Introduction
1.1. Genetics: State of the art
1.1.1. Genomics approaches and results
1.1.2. Metabolomics
1.2. Pharmacogenetics: State of the art
2. Role of SLC6A3 gene as a potential genetic biomarker in ADHD diagnosis in children and adults
2.1. Methods
2.1.1. Search strategy and selection
2.1.2. Inclusion and exclusion criteria
2.1.3. Data extraction for meta-analyses and statistical analyses
2.2. Results
3. Role of SLC6A3 gene as potential mediator/predictor of the MPH drug response in children and in adults
3.1. Methods
3.1.1. Search strategy and selection
3.1.2. Inclusion and exclusion criteria
3.1.3. Data extraction for meta-analysis
3.1.4. Statistical analyses
3.2. Results
4. Discussion and future research
5. Conclusion
References
Chapter 22: Genomics of Alzheimers disease
1. Introduction
2. The genetics of AD
3. Other omics approaches
4. Conclusion
References
Chapter 23: Current progress and future direction in the genetics of PTSD: Focus on the development and contributions of ...
1. Introduction
2. Behavioral genetics of PTSD
3. Molecular genetic studies
3.1. Candidate gene studies
3.2. cGxE studies
3.3. Genome-wide association studies
4. Psychiatric Genomics Consortium
5. Initial findings from the PGC-PTSD
6. Unique considerations of the PGC-PTSD
7. Development of the PGC-PTSD working groups
7.1. Gene expression working group
7.2. EWAS working group
7.3. Neuro-imaging working group
7.4. Physical health working group
7.5. CNV working group
8. Future directions of the PGC-PTSD and PTSD genomics as a whole
References
Chapter 24: Genomic contributions to anxiety disorders
1. Introduction
2. Family and twin studies
3. Linkage studies
4. Candidate gene association studies
5. Genome-wide association studies
6. Post-GWAS analysis
7. Expression analysis
8. Epigenetics
9. Next-generation sequencing
10. Copy number variation
11. Pharmacogenomics
12. Future directions
13. Conclusions
References
Chapter 25: Proteomics for diagnostic and therapeutic blood biomarker discovery in schizophrenia and other psychotic diso ...
1. Proteomics for complex psychiatric disorders
2. Proteomics and personalized psychiatry
3. Mass spectrometry workflows
4. Mass spectrometry subtypes
5. Acquisition methods for proteomic data
6. Multiplex immunoassays
7. A comparison between mass spectrometry and multiplex immunoassay
8. Data analysis and bioinformatics
9. Functional analysis of proteomic data
10. Proteomics for blood biomarker discovery in psychiatry
10.1. The search for schizophrenia biomarkers: An example of psychiatric biomarker discovery
11. Conclusion
References
Chapter 26: Molecular biomarkers in depression: Toward personalized psychiatric treatment
1. Personalized psychiatry-A pipedream or a possibility?
1.1. Challenges in diagnosis
1.2. A role for biomarkers?
2. Noncoding RNAs
2.1. MicroRNAs
2.2. Long noncoding RNAs
2.3. Perspective
3. Coding transcriptomic and proteomic biomarkers
3.1. Neurotransmitters
3.1.1. Serotonin
3.1.2. Dopamine
3.1.3. Glutamate
3.1.4. GABA
3.2. BDNF and growth factors
3.2.1. Brain-derived neurotrophic factor
3.2.2. Vascular endothelial growth factor
3.2.3. Fibroblast growth factor
3.2.4. VGF
3.3. Biomarkers associated with the HPA axis
3.3.1. Cortisol
3.3.2. Corticotropic release factor
3.3.3. Adrenocorticotrophic hormone
3.4. Inflammatory biomarkers
3.5. Perspective
4. Genomic biomarkers
4.1. Mitochondrial DNA
4.2. Telomeric biomarkers
5. The gut microbiome
6. Concluding remarks
References
Further Reading
Chapter 27: Neuroimaging biomarkers of late-life major depressive disorder pathophysiology, pathogenesis, and treatment r ...
1. Neuroimaging biomarkers of LLD pathophysiology
1.1. Section summary
1.2. White matter biomarkers
1.3. Gray matter biomarkers
1.4. Functional activation biomarkers
1.5. Functional connectivity biomarkers
1.6. Cerebral blood flow biomarkers
2. Neuroimaging biomarkers of LLD pathogenesis
2.1. Section summary
2.2. White matter integrity and depression development
2.3. Regional brain volume and depression development
3. Neuroimaging biomarkers of LLD treatment response
3.1. Section summary
3.2. White matter integrity and treatment response
3.3. Regional brain volume and treatment response
3.4. Functional activation and treatment response
3.5. Functional connectivity and treatment response
3.6. Cerebral blood flow and treatment response
4. Machine learning to predict LLD and treatment response
5. Conclusion and future directions
Acknowledgments
References
Chapter 28: Copy number variants in psychiatric disorders
1. Copy number variation and the human genome
2. Identification of copy number variants
3. Copy number variants and study designs
4. Copy number variants and clinical translation efforts
5. Concluding remarks and future perspectives
Conflict of interest
References
Chapter 29: Gene-environment interaction in psychiatry
1. Introduction
2. Environmental conditions
3. Candidate genes
4. Depressive disorders and anxiety
5. The insertion/deletion polymorphism of the serotonin transporter gene (5-HTTLPR)
6. FKBP5 gene
7. Brain-derived neurotrophic factor (BDNF)
8. Genome-wide challenges of GxE
9. Schizophrenia and bipolar disorder
10. Future relevance of GxE interaction
References
Chapter 30: Epigenetics: A new approach to understanding mechanisms in depression and to predict antidepressant treatment ...
1. The main epigenetic processes that may be used as biomarkers for treatment response are DNA modifications, noncoding R ...
2. DNA methylation, stress, and depression
3. Brain-derived neurotrophic factor and depression
4. Methylation patterns as biomarkers for depression
5. BDNF methylation and antidepressant response
6. BDNF modifications and response to ECT
7. Upregulation of BDNF signaling and antidepressant treatment response to ECT
8. Conclusion and outlook
References
Chapter 31: Gene coexpression network and machine learning in personalized psychiatry
1. Introduction
2. Diagnosis
2.1. Coexpression network analysis to differentiate disease from healthy controls
2.2. Weighted gene coexpression network analysis
2.3. Differential coexpression network analysis to study differences among gene interconnections
3. Diagnosis of patient subtypes
3.1. Biclustering for identifying subgroups of patients
4. Personalized treatment
4.1. Gene coexpression networks for understanding treatment response biotypes
5. Disease mechanisms
5.1. Gene coexpression network for integrative analyses in psychiatry
5.2. Shared molecular mechanisms among disorders: Implications for treatment response
6. Future methods: Machine learning
6.1. Gene selection and complex trait prediction
6.2. Applications for gene expression
Conclusions
References
Chapter 32: Pharmacogenomics of bipolar disorder
1. Genetic bases of BD
2. Clinical heterogeneity of BD and pharmacological treatments
3. Pharmacogenetics of lithium: Candidate genes studies
3.1. Neurotransmitter systems
3.2. Neurothrophic factors
3.3. Inositol signaling pathway
3.4. GSK-3 and circadian clock rhythms
4. Pharmacogenetics of lithium: Genome-wide association studies
5. Pharmacogenetics of other mood stabilizers
6. Pharmacogenetics of mood stabilizers: Focus on side effects
7. Conclusions and future perspective
References
Further Reading
Chapter 33: Pharmacogenomics of treatment response in major depressive disorder
1. Introduction
2. Candidate gene pharmacogenetic studies of antidepressant response
2.1. Pharmacokinetic candidate genes
2.2. Pharmacodynamic candidate genes
3. GWAS of antidepressant response
4. Antidepressant pharmacogenomics study design and definition of treatment outcomes: Impact on research findings and cli ...
5. Novel approaches to identifying pharmacogenomic effects and their application in studies of antidepressant response
6. Antidepressant pharmacogenomics resources
7. The emergence of pharmacogenomic tests to individualize treatment of MDD
8. Future considerations
9. Conclusion
References
Chapter 34: Genomic treatment response prediction in schizophrenia
1. Introduction
1.1. Burden of TRS
1.2. Clozapine treatment
2. Prediction of TRS
3. Genomic prediction of TRS
3.1. Heritability of TRS
3.2. Candidate studies
3.3. GWAS
3.4. Genetic liability to schizophrenia
3.5. Rare variant studies
4. Glutamate hypothesis of TRS
5. Challenges and future directions
6. Conclusions
References
Chapter 35: Personalized treatment in bipolar disorder
1. Introduction
2. Tools for personalized treatment in BD
2.1. Clinical and psychobiographical features
2.2. Biological variables
2.2.1. Molecular markers
2.2.2. Neuroimaging markers
2.2.3. Genetic markers
2.2.3.1. Pharmacogenomics
Alterations in drug-metabolizing enzymes
Candidate genes related to treatment response
Candidate genes related to adverse events due to treatment
2.2.4. Epigenetic markers
2.3. Technology and personalized psychiatry
3. Ethical issues in personalized psychiatry
4. Summary and future directions
References
Chapter 36: Genetic testing in psychiatry: State of the evidence
1. Introduction
2. Types of genetic tests
3. Current evidence base
3.1. Genetic tests for identifying ``at risk´´ individuals
3.2. Genetic tests for assisting with diagnosis
3.3. Genetic tests to guide optimal therapy
3.3.1. HLA-B/HLA-A-Carbamazepine
3.3.2. CYP2D6/CYP2C19-Antidepressants
3.3.3. CYP2D6-Antipsychotics
3.3.4. Promising gene-drug interactions
3.3.5. Commercial pharmacogenetic testing
4. Strategies for strengthening the evidence base
4.1. Phenotype definition and measurement
4.2. Pharmacogenetic testing panel and reporting standardization
4.3. Innovative clinical trials
5. Conclusion
References
Chapter 37: Opportunities and challenges of implementation models of pharmacogenomics in clinical practice
1. Introduction
2. Barriers to implementing pharmacogenetic tests
2.1. Access to laboratory and analytical validity
2.2. Establishing genotyping
2.3. Cost effectiveness
2.4. Ethical considerations
2.5. Providers, payers, and industry perspectives
3. Education and opportunities to implement pharmacogenetic tests
3.1. Education-Patients
3.2. Education-Physicians
3.3. Education-Nurses
3.4. Education-Pharmacists
4. Pharmacogenetic testings role in collaborative care and shared decision making
5. The ``IMPACT´´ study at the Centre for Addiction and Mental Health in Toronto
6. Conclusion
References
Further reading
Chapter 38: Metabolomics in psychiatry
1. Why metabolomics in psychiatry?
2. Application of metabolomics in psychiatry
2.1. Disease diagnosis
2.2. Treatment response
2.2.1. Pharmacometabolomics
2.2.2. Drug discovery and development
2.3. Disease monitoring
3. Limitations of metabolomics in psychiatry
3.1. Environmental variations
3.2. Unknown metabolites
3.3. Availability and application of metabolomic assays in clinic
4. Future vistas
4.1. The human metabolome project
4.2. Integration of other "omics
4.3. Personalized medicine
References
Chapter 39: Real-time fMRI brain-computer interface: A tool for personalized psychiatry?
1. Introduction
2. Principles and methods
3. Clinical applications of fMRI-NF
4. A personalized approach
4.1. Personalized neural targets
4.2. Personalized stimuli and other elements of the neurofeedback therapy
5. Concluding remarks
Acknowledgment
References
Chapter 40: How functional neuroimaging can be used for prediction and evaluation in psychiatry
1. Understanding the neural basis of mental health conditions
2. Identification of individuals at high risk of developing a psychiatric condition
3. Differential diagnosis
4. Treatment choice and mechanisms of drug action
5. General remarks
References
Further reading
Chapter 41: Neuroimaging, genetics, and personalized psychiatry: Developments and opportunities from the ENIGMA consortium
1. Introduction
2. Underpowered studies and the crisis of reproducibility
3. The enhancing neuroimaging genetics through meta-analysis consortium
4. ENIGMA imaging genomics and genome-wide association studies
5. ENIGMA psychiatric neuroimaging studies
6. Toward personalized psychiatry
7. Challenges of large-scale data-sharing
8. Conclusions
8.1. Future directions
8.2. Conclusions
Acknowledgment
References
Further reading
Chapter 42: Applying a neural circuit taxonomy in depression and anxiety for personalized psychiatry
1. Getting a handle on our terminology for precision psychiatry
2. Foundations for a precision psychiatry taxonomy for depression and anxiety based on neural circuits
3. ``Default Mode´´ circuit
3.1. Default mode circuit disruptions in depression and anxiety
3.2. Default mode circuit and treatment implications
4. ``Salience´´ circuit
4.1. Salience circuit disruptions in depression and anxiety
4.2. Salience circuit and treatment implications
5. Affective circuits
6. Negative Affect
6.1. Negative affective circuit disruptions in depression and anxiety
6.2. Negative affective circuit and treatment implications
7. Positive affect circuit: ``Reward´´
7.1. Reward circuit disruptions in depression and anxiety
7.2. Reward circuit and treatment implications
8. Attention circuit
8.1. Attention circuit disruptions in depression and anxiety
8.2. Attention circuit and treatment implications
9. Cognitive control circuit
9.1. Cognitive control circuit disruptions in depression and anxiety
9.2. Cognitive control circuit and treatment implications
10. Future directions to close the clinical translational gap
10.1. Standardized protocols
10.2. Normative data
10.3. Integration across modalities within the same patients
10.4. Computational innovation
10.5. Prospective, circuit-guided interventions
11. Conclusions
Acknowledgments
References
Chapter 43: Multimodal modeling for personalized psychiatry
1. Introduction
2. Modes of data in psychiatry
2.1. Clinical data
2.1.1. Collection and quantification of clinical data in psychiatry
2.2. Cognitive and general function
2.3. Peripheral biomarkers
2.4. Brain imaging and electrophysiology
3. Multivariate modeling
3.1. Study formulation and design
3.2. Model training and testing
3.2.1. Variable selection and coding
3.2.2. Model selection (estimation)
3.2.3. Model assessment
3.3. Limitations
3.4. Conclusions and future directions
References
Chapter 44: Standardized biomarker and biobanking requirements for personalized psychiatry
1. Personalized psychiatry-from diagnosis to treatment response
2. Fundamentals of biobanking relevant to mental health disorders
2.1. Implementation of a business plan to ensure sustainability
2.1.1. Financial provisions
2.1.2. Organizational structure and governance model
2.1.3. Biobank succession and disassembly
2.1.4. Reporting strategies
2.1.5. Consumer engagement
2.2. Regulatory governance
2.2.1. Ethical considerations
2.2.2. Generation of global participant data networks within ethical and legislative frameworks
2.2.3. Participant consent form requirements in psychiatry
2.2.4. Incidental findings
2.3. Sample management and infrastructure for high quality storage of biospecimens
2.3.1. Importance of quality control and standardized protocols for a biobank
2.3.2. Methods for deidentification of participant data
2.3.3. Biobanking data management and integration strategies
2.3.4. Psychiatric biospecimen storage and handling
2.3.5. Recording of biospecimen information
2.3.6. Sharing biospecimens
2.3.7. Transfer of biospecimens to internal and external sites
2.3.8. Associated clinical data
3. Reproducible identification of biomarker signatures within mental health disorders
3.1. Biomarker discovery in psychiatry-considerations, stage I
3.2. Biomarker discovery in psychiatry-considerations stage II
3.2.1. Participant recruitment
3.2.2. Biospecimen collection and processing
3.2.3. Biospecimen shipping and storage for preservation of biomarker integrity
3.3. Biomarker discovery in psychiatry-considerations, stage III
3.3.1. Reporting and sharing results
3.4. Future vision for biomarker discovery and validation in psychiatry
References
Chapter 45: Ethical, policy, and research considerations for personalized psychiatry
1. Introduction
2. Ethics
2.1. Big data
2.2. Health disparities
3. Health policy
3.1. Biobanking
3.2. High quality clinical trials
3.3. Reimbursement
3.4. Regulation
3.5. Information system upgrades
4. Medical research
4.1. Research considerations for personalized psychiatry
5. Conclusion
References
Chapter 46: The future of personalized psychiatry
Index
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