How far can mice carry autism research?

In the face of growing controversy about the utility of genetic mouse models of human disease, Rothwell et al. report on a shared mechanism by which two different neuroligin-3 mutations, associated with autism spectrum disorders in humans, produce an enhancement in motor learning. The open question is how much we can learn about human ills from such models.

A bone to pick with compulsive behavior.

Mice with mutations in the Hoxb8 gene exhibit compulsive grooming behavior. Chen et al. (2010) now report that this behavior stems from Hoxb8 deficiency in microglia, a type of immune cell in the brain derived from bone marrow. These findings provide intriguing connections between immune dysfunction and neuropsychiatric disorders.

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.

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.

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.

DSM-5 and RDoC: progress in psychiatry research?

Neuroscience studies into psychiatric disorders generally rely on disease definitions that are based on the influential Diagnostic and Statistical Manual of Mental Disorders (DSM), the fifth edition of which (DSM-5) was released earlier this year. Designed as a purely diagnostic tool, the DSM considers different disorders as distinct entities. However, boundaries between disorders are often not as strict as the DSM suggests. To provide an alternative framework for research into psychiatric disorders, the US National Institute of Mental Health (NIMH) has recently introduced its Research Domain Criteria (RDoC) project. In the RDoC, five 'domains' each reflect a brain system in which functioning is impaired, to different degrees, in different psychiatric conditions. Nature Reviews Neuroscience asked six leading investigators for their thoughts on how DSM-5 and the RDoC will influence neuroscience research into psychiatric disorders.

Genetic research in autism spectrum disorders.

PURPOSE OF REVIEW: The recent explosion of genetic findings in autism spectrum disorder (ASD) research has improved knowledge of the disorder's underlying biology and etiologic architecture. This review introduces concepts and results from recent genetic studies and discusses the manner in which those findings can influence the trajectory of ASD research. RECENT FINDINGS: Large consortium studies have associated ASDs with many types of genetic risk factors, including common polygenic risk, de novo single nucleotide variants, copy number variants, and rare inherited variants. In aggregate, these results confirm the heterogeneity and complexity of ASDs. The rare variant findings in particular point to genes and pathways that begin to bridge the gap between behavior and biology. SUMMARY: Genetic studies have the potential to identify the biological underpinnings of ASDs and other neuropsychiatric disorders. The data they generate are already being used to examine disease pathways and pathogenesis. The results also speak to ASD heterogeneity and, in the future, may be used to stratify research studies and treatment trials.

A glimmer of light for neuropsychiatric disorders.

Understanding the pathogenesis of neuropsychiatric disorders is a substantial challenge for neurobiologists. It has long been hoped that identifying alleles that confer increased risk of such disorders would provide clues for neurobiological investigation. But this quest has been stymied by a lack of validated biological markers for characterizing and distinguishing the different disorders and by the genetic complexity underpinning these diseases. Now, modern genomic technologies have begun to facilitate the discovery of relevant genes.

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.

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.

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.

Computational roles for dopamine in behavioural control.

Neuromodulators such as dopamine have a central role in cognitive disorders. In the past decade, biological findings on dopamine function have been infused with concepts taken from computational theories of reinforcement learning. These more abstract approaches have now been applied to describe the biological algorithms at play in our brains when we form value judgements and make choices. The application of such quantitative models has opened up new fields, ripe for attack by young synthesizers and theoreticians.

The diagnosis of mental disorders: the problem of reification.

A pressing need for interrater reliability in the diagnosis of mental disorders emerged during the mid-twentieth century, prompted in part by the development of diverse new treatments. The Diagnostic and Statistical Manual of Mental Disorders (DSM), third edition answered this need by introducing operationalized diagnostic criteria that were field-tested for interrater reliability. Unfortunately, the focus on reliability came at a time when the scientific understanding of mental disorders was embryonic and could not yield valid disease definitions. Based on accreting problems with the current DSM-fourth edition (DSM-IV) classification, it is apparent that validity will not be achieved simply by refining criteria for existing disorders or by the addition of new disorders. Yet DSM-IV diagnostic criteria dominate thinking about mental disorders in clinical practice, research, treatment development, and law. As a result, the modern DSM system, intended to create a shared language, also creates epistemic blinders that impede progress toward valid diagnoses. Insights that are beginning to emerge from psychology, neuroscience, and genetics suggest possible strategies for moving forward.

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).