Study Protocol: Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project.

BACKGROUND: Objective and quantifiable markers are crucial for developing novel therapeutics for mental disorders by 1) stratifying clinically similar patients with different underlying neurobiological deficits and 2) objectively tracking disease trajectory and treatment response. Schizophrenia is often confounded with other psychiatric disorders, especially bipolar disorder, if based on cross-sectional symptoms. Awake and sleep EEG have shown promise in identifying neurophysiological differences as biomarkers for schizophrenia. However, most previous studies, while useful, were conducted in European and American populations, had small sample sizes, and utilized varying analytic methods, limiting comprehensive analyses or generalizability to diverse human populations. Furthermore, the extent to which wake and sleep neurophysiology metrics correlate with each other and with symptom severity or cognitive impairment remains unresolved. Moreover, how these neurophysiological markers compare across psychiatric conditions is not well characterized. The utility of biomarkers in clinical trials and practice would be significantly advanced by well-powered transdiagnostic studies. The Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project aims to address these questions through a large, multi-center cohort study involving East Asian populations. To promote transparency and reproducibility, we describe the protocol for the GRINS project. METHODS: The research procedure consists of an initial screening interview followed by three subsequent sessions: an introductory interview, an evaluation visit, and an overnight neurophysiological recording session. Data from multiple domains, including demographic and clinical characteristics, behavioral performance (cognitive tasks, motor sequence tasks), and neurophysiological metrics (both awake and sleep electroencephalography), are collected by research groups specialized in each domain. CONCLUSION: Pilot results from the GRINS project demonstrate the feasibility of this study protocol and highlight the importance of such research, as well as its potential to study a broader range of patients with psychiatric conditions. Through GRINS, we are generating a valuable dataset across multiple domains to identify neurophysiological markers of schizophrenia individually and in combination. By applying this protocol to related mental disorders often confounded with each other, we can gather information that offers insight into the neurophysiological characteristics and underlying mechanisms of these severe conditions, informing objective diagnosis, stratification for clinical research, and ultimately, the development of better-targeted treatment matching in the clinic.

Shifting the trajectory of therapeutic development for neurological and psychiatric disorders.

Clinical trials for central nervous system disorders often enroll patients with unrecognized heterogeneous diseases, leading to costly trials that have high failure rates. Here, we discuss the potential of emerging technologies and datasets to elucidate disease mechanisms and identify biomarkers to improve patient stratification and monitoring of disease progression in clinical trials for neuropsychiatric disorders. Greater efforts must be centered on rigorously standardizing data collection and sharing of methods, datasets, and analytical tools across sectors. To address health care disparities in clinical trials, diversity of genetic ancestries and environmental exposures of research participants and associated biological samples must be prioritized.

The Hierarchical Taxonomy of Psychopathology (HiTOP) in psychiatric practice and research.

The Hierarchical Taxonomy of Psychopathology (HiTOP) has emerged out of the quantitative approach to psychiatric nosology. This approach identifies psychopathology constructs based on patterns of co-variation among signs and symptoms. The initial HiTOP model, which was published in 2017, is based on a large literature that spans decades of research. HiTOP is a living model that undergoes revision as new data become available. Here we discuss advantages and practical considerations of using this system in psychiatric practice and research. We especially highlight limitations of HiTOP and ongoing efforts to address them. We describe differences and similarities between HiTOP and existing diagnostic systems. Next, we review the types of evidence that informed development of HiTOP, including populations in which it has been studied and data on its validity. The paper also describes how HiTOP can facilitate research on genetic and environmental causes of psychopathology as well as the search for neurobiologic mechanisms and novel treatments. Furthermore, we consider implications for public health programs and prevention of mental disorders. We also review data on clinical utility and illustrate clinical application of HiTOP. Importantly, the model is based on measures and practices that are already used widely in clinical settings. HiTOP offers a way to organize and formalize these techniques. This model already can contribute to progress in psychiatry and complement traditional nosologies. Moreover, HiTOP seeks to facilitate research on linkages between phenotypes and biological processes, which may enable construction of a system that encompasses both biomarkers and precise clinical description.

Use of mouse models to investigate the contributions of CNVs associated with schizophrenia and autism to disease mechanisms.

Human genetics is providing much needed clues to mechanisms underlying neuropsychiatric disorders. Highly penetrant copy number variants (CNVs) were among the first genetic variants confidently associated with schizophrenia and autism spectrum disorders (ASDs). Despite their structural complexity, the high penetrance of CNVs associated with neuropsychiatric disorders suggested utility for construction of cellular and animal models. Human cellular models that carry disease associated alleles have the advantage of human genetic backgrounds against which to study the effects of CNVs. However, investigation of the effects of disease-associated alleles on the structure and function of living brains requires genome engineering of model organisms or introduction of genetic material into their brains by viral vectors. Here I focus on the translational utility of transgenic mice that carry models of human neuropsychiatric CNVs, while recognizing their limitations as veridical models of complex human brain disorders. In order to improve translational utility and avoid the intellectual cul-de-sacs that often bedevil interpretation of neuropsychiatric disease models, I conclude with a 'draft' proposal to replace current concepts of construct and face validity with more nuanced and contextually relevant judgments.

Psychiatric Disorders: Grounded in Human Biology but Not Natural Kinds.

The third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III) and its descriptive psychiatry-based intellectual antecedents imagined psychiatric disorders as discontinuous categories, presumably natural kinds, that would be empirically validated based on future scientific studies. Validation would emerge from a predicted convergence of clinical descriptions (symptom clusters that could be shown to be stable over the lifespan), laboratory results, and family studies. That future science is now arriving, but rather than validating the categorical DSM approach, large-scale genetics along with modern neurobiology and epidemiology have emphatically undercut it. Clinical description, laboratory studies, and family (now genetic) studies do not converge at all on distinct categories. Rather, modern studies are consistent with psychiatric disorders as heterogeneous quantitative deviations from health. The characteristics of these disorders have proven to be discoverable rather than invented and thus are grounded in nature. However, scientific results demonstrate that psychiatric disorders cannot reasonably be understood as discrete categories-and certainly not as natural kinds.

Counterpoint. Early intervention for psychosis risk syndromes: Minimizing risk and maximizing benefit.

BACKGROUND: Malhi et al. in this issue critique the clinical high risk (CHR) syndrome for psychosis. METHOD: Response to points of critique. RESULTS: We agree that inconsistency in CHR nomenclature should be minimized. We respectfully disagree on other points. In our view: a) individuals with CHR and their families need help, using existing interventions, even though we do not yet fully understand disease mechanisms; b) substantial progress has been made in identification of biomarkers; c) symptoms used to identify CHR are specific to psychotic illnesses; d) CHR diagnosis is not "extremely difficult"; e) the pattern of progression, although heterogenous, is discernible; f) "psychosis-like symptoms" are common but are not used to identify CHR; and g) on the point described as 'the real risk,' CHR diagnosis does not frequently cause harmful stigma. DISCUSSION: Malhi et al.'s arguments do not fairly characterize progress in the CHR field nor efforts to minimize stigma. That said, much work remains in areas of consistent nomenclature, mechanisms of disease, dissecting heterogeneity, and biomarkers. With regard to what the authors term the "real risk" of stigma associated with a CHR "label," however, our view is that avoiding words like "risk" and "psychosis" reinforces the stigma that both they and we mean to oppose. Moreover, patients and their families benefit from being given a term that describes what is happening to them.

Redefining phenotypes to advance psychiatric genetics: Implications from hierarchical taxonomy of psychopathology.

Genetic discovery in psychiatry and clinical psychology is hindered by suboptimal phenotypic definitions. We argue that the hierarchical, dimensional, and data-driven classification system proposed by the Hierarchical Taxonomy of Psychopathology (HiTOP) consortium provides a more effective approach to identifying genes that underlie mental disorders, and to studying psychiatric etiology, than current diagnostic categories. Specifically, genes are expected to operate at different levels of the HiTOP hierarchy, with some highly pleiotropic genes influencing higher order psychopathology (e.g., the general factor), whereas other genes conferring more specific risk for individual spectra (e.g., internalizing), subfactors (e.g., fear disorders), or narrow symptoms (e.g., mood instability). We propose that the HiTOP model aligns well with the current understanding of the higher order genetic structure of psychopathology that has emerged from a large body of family and twin studies. We also discuss the convergence between the HiTOP model and findings from recent molecular studies of psychopathology indicating broad genetic pleiotropy, such as cross-disorder SNP-based shared genetic covariance and polygenic risk scores, and we highlight molecular genetic studies that have successfully redefined phenotypes to enhance precision and statistical power. Finally, we suggest how to integrate a HiTOP approach into future molecular genetic research, including quantitative and hierarchical assessment tools for future data-collection and recommendations concerning phenotypic analyses. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Comparative genetic architectures of schizophrenia in East Asian and European populations.

Schizophrenia is a debilitating psychiatric disorder with approximately 1% lifetime risk globally. Large-scale schizophrenia genetic studies have reported primarily on European ancestry samples, potentially missing important biological insights. Here, we report the largest study to date of East Asian participants (22,778 schizophrenia cases and 35,362 controls), identifying 21 genome-wide-significant associations in 19 genetic loci. Common genetic variants that confer risk for schizophrenia have highly similar effects between East Asian and European ancestries (genetic correlation = 0.98 ± 0.03), indicating that the genetic basis of schizophrenia and its biology are broadly shared across populations. A fixed-effect meta-analysis including individuals from East Asian and European ancestries identified 208 significant associations in 176 genetic loci (53 novel). Trans-ancestry fine-mapping reduced the sets of candidate causal variants in 44 loci. Polygenic risk scores had reduced performance when transferred across ancestries, highlighting the importance of including sufficient samples of major ancestral groups to ensure their generalizability across populations.

The NIH BRAIN Initiative: Integrating Neuroethics and Neuroscience.

The NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative is focused on developing new tools and neurotechnologies to transform our understanding of the brain, and neuroethics is an essential component of this research effort. Coordination with other brain projects around the world will help maximize success.

Predicting Polygenic Risk of Psychiatric Disorders.

Genetics provides two major opportunities for understanding human disease-as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms. Genetic biomarkers are therefore especially promising for psychiatric disorders. Genome-wide association studies of common diseases have matured over the last decade, generating the knowledge base for increasingly informative individual-level genetic risk prediction. In this review, we discuss fundamental concepts involved in computing genetic risk with current methods, strengths and weaknesses of various approaches, assessments of utility, and applications to various psychiatric disorders and related traits. Although genetic risk prediction has become increasingly straightforward to apply and common in published studies, there are important pitfalls to avoid. At present, the clinical utility of genetic risk prediction is still low; however, there is significant promise for future clinical applications as the ancestral diversity and sample sizes of genome-wide association studies increase. We discuss emerging data and methods aimed at improving the value of genetic risk prediction for disentangling disease mechanisms and stratifying subjects for epidemiological and clinical studies. For all applications, it is absolutely critical that polygenic risk prediction is applied with appropriate methodology and control for confounding to avoid repeating some mistakes of the candidate gene era.

Innovations and changes in the ICD-11 classification of mental, behavioural and neurodevelopmental disorders.

Following approval of the ICD-11 by the World Health Assembly in May 2019, World Health Organization (WHO) member states will transition from the ICD-10 to the ICD-11, with reporting of health statistics based on the new system to begin on January 1, 2022. The WHO Department of Mental Health and Substance Abuse will publish Clinical Descriptions and Diagnostic Guidelines (CDDG) for ICD-11 Mental, Behavioural and Neurodevelopmental Disorders following ICD-11's approval. The development of the ICD-11 CDDG over the past decade, based on the principles of clinical utility and global applicability, has been the most broadly international, multilingual, multidisciplinary and participative revision process ever implemented for a classification of mental disorders. Innovations in the ICD-11 include the provision of consistent and systematically characterized information, the adoption of a lifespan approach, and culture-related guidance for each disorder. Dimensional approaches have been incorporated into the classification, particularly for personality disorders and primary psychotic disorders, in ways that are consistent with current evidence, are more compatible with recovery-based approaches, eliminate artificial comorbidity, and more effectively capture changes over time. Here we describe major changes to the structure of the ICD-11 classification of mental disorders as compared to the ICD-10, and the development of two new ICD-11 chapters relevant to mental health practice. We illustrate a set of new categories that have been added to the ICD-11 and present the rationale for their inclusion. Finally, we provide a description of the important changes that have been made in each ICD-11 disorder grouping. This information is intended to be useful for both clinicians and researchers in orienting themselves to the ICD-11 and in preparing for implementation in their own professional contexts.

The importance of true collaboration in efforts to increase diversity in genetic analyses.

In Michael Gross's recent article ('Mind the genome diversity gap'), he rightly states that global health equity demands an overhaul of the current approach to genetic analysis of psychiatric conditions, which relies heavily on European sample collections. Unfortunately, the article missed the mark in its description of work undertaken by the Broad Institute of MIT and Harvard, the Harvard T.H. Chan School of Public Health, and collaborative partners in Africa and Asia that aims to change the status quo.

Publisher Correction: Whole genome sequencing in psychiatric disorders: the WGSPD consortium.

In the version of this article initially published, the consortium authorship and corresponding authors were not presented correctly. In the PDF and print versions, the Whole Genome Sequencing for Psychiatric Disorders (WGSPD) consortium was missing from the author list at the beginning of the paper, where it should have appeared as the seventh author; it was present in the author list at the end of the paper, but the footnote directing readers to the Supplementary Note for a list of members was missing. In the HTML version, the consortium was listed as the last author instead of as the seventh, and the line directing readers to the Supplementary Note for a list of members appeared at the end of the paper under Author Information but not in association with the consortium name itself. Also, this line stated that both member names and affiliations could be found in the Supplementary Note; in fact, only names are given. In all versions of the paper, the corresponding author symbols were attached to A. Jeremy Willsey, Steven E. Hyman, Anjene M. Addington and Thomas Lehner; they should have been attached, respectively, to Steven E. Hyman, Anjene M. Addington, Thomas Lehner and Nelson B. Freimer. As a result of this shift, the respective contact links in the HTML version did not lead to the indicated individuals. The errors have been corrected in the HTML and PDF versions of the article.