In 1800, mania was conceptualized as an agitated psychotic state. By 1900, it closely resembled its modern form. This paper reviews the descriptions of mania in Western psychiatry from 1880 to 1900, when Kraepelin was training and developing his concept of manic-depressive illness. Psychiatric textbooks published 1900–1960 described 22 characteristic manic symptoms/signs the presence of which were recorded in 25 psychiatric textbooks and three other key documents published 1880–1900. Descriptions of mania in these nineteenth century textbooks closely resembled those in the twentieth century, recording a mean (s.d.) of 15.9 (2.3) and 17.0 (2.3) of the characteristic symptoms, respectively (p = 0.12). The frequency with which individual symptoms were reported was substantially correlated in these two periods (r = +0.64). Mendel's 1881 monograph, Kraepelin's first description of mania in 1883 and the entry for mania in Tuke's Dictionary of Psychological Medicine (1892) described a mean (s.d.) of 19 (1.7) of these characteristic symptoms. These descriptions of mania often contained phenomenologically rich descriptions of euphoria, hyperactivity, grandiosity, flight of ideas, and poor judgment. They also emphasized several features not in DSM criteria including changes in character, moral standards and physical appearance, and increased sense of humor and sexual drive. Fifteen authors described key symptoms/signs of mania most reporting elevated mood, motoric hyperactivity and accelerated mental processes. By 1880, the syndrome of mania had been largely stabilized in its modern form. In the formation of his concept of manic-depressive illness, Kraepelin utilized the syndrome of mania as described in the psychiatric community in which he was trained.

The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.

In recent years there has been growing interest in early intervention in psychotic disorders and a number of clinical and research programmes have been developed. The clinical staging model has been an essential part of early intervention as it provides the rationale of existing programmes. In medicine, clinical staging is a valuable approach in disorders where primary pathology is progressive in nature. However, the clinical staging model of psychosis has been proposed without establishing first that schizophrenia is a primarily progressive disorder. In reviewing existing evidence, this current paper argues that cross-sectional data interpreted as supportive of clinical staging data does not consider the effects of sampling bias, problems in reliability in assessing ‘soft symptoms’, or false positives. Longitudinal neurobiological studies do not provide a convincing case for primarily progressive pathology in schizophrenia. Clinical progression in schizophrenia can be better conceptualised as neuroplastic changes in response to interaction between core developmental pathology and environmental stimuli. An alternative rationale for early and continuous intervention targeting neurodevelopmental abnormality and neuroplastic changes, as well as medical and psychological comorbidities, is proposed in this paper.

Auditory hallucinations (AH) are often considered a sign of a psychotic disorder. This is promoted by the DSM-5 category of Other Specified Schizophrenia Spectrum And Other Psychotic Disorder (OSSSOPD), the diagnostic criteria for which are fulfilled with the sole presence of persistent AH, in the absence of any other psychotic symptoms. And yet, persistent AH are not synonymous with having a psychotic disorder, and should therefore not be uncritically treated as such. Many people who seek treatment for persistent AH have no other psychotic symptoms, have preserved reality-testing capacities, and will never develop a schizophrenia spectrum disorder. Instead, hallucinations may be the result of many different causes, including borderline personality disorder, post-traumatic stress disorder (PTSD), hearing loss, sleep disorders or brain lesions, and they may even occur outside the context of any demonstrable pathology. In such cases, the usage of the DSM-5 diagnosis of OSSSOPD would be incorrect, and it may prompt unwarranted treatment with antipsychotic medication. We therefore argue that a DSM-5 diagnosis of Schizophrenia Spectrum Disorder (or any other type of psychotic disorder) characterized by AH should require at least one more symptom listed under the A-criterion (i.e. delusions, disorganized speech, disorganized or catatonic behavior or negative symptoms). Adhering to these more stringent criteria may help to distinguish between individuals with persistent AH which are part of a psychotic disorder, for whom antipsychotic medication may be helpful, and individuals with AH in the absence of such a disorder who may benefit from other approaches (e.g. different pharmacological interventions, improving coping style, trauma-related therapy).

Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.

Lewy body dementia (consisting of dementia with Lewy bodies and Parkinson's disease dementia) is a common neurodegenerative disease characterised by visual hallucinations, fluctuating attention, motor disturbances, falls, and sensitivity to antipsychotics. This combination of features presents challenges for pharmacological management. Given this, we sought to review evidence for non-pharmacological interventions with patients with Lewy body dementia and their carers. Bibliographic databases were searched using a wide range of search terms and no restrictions were placed on study design, language, or clinical setting. Two reviewers independently assessed papers for inclusion, rated study quality, and extracted data. The search identified 21 studies including two randomised controlled trials with available subgroup data, seven case series, and 12 case studies. Most studies reported beneficial effects of the interventions used, though the only sizeable study was on dysphagia, showing a benefit of honey-thickened liquids. Given the heterogeneity of interventions and poor quality of the studies overall, no quantitative synthesis was possible. Overall, identified studies suggested possible benefits of non-pharmacological interventions in Lewy body dementia, but the small sample sizes and low quality of studies mean no definite recommendations can be offered. Our findings underscore the clear and urgent need for future research on this topic.

The neurobiological understanding of mood, and by extension mood disorders, remains elusive despite decades of research implicating several neuromodulator systems. This review considers a new approach based on existing theories of functional brain organisation. The free energy principle (a.k.a. active inference), and its instantiation in the Bayesian brain, offers a complete and simple formulation of mood. It has been proposed that emotions reflect the precision of – or certainty about – the predicted sensorimotor/interoceptive consequences of action. By extending this reasoning, in a hierarchical setting, we suggest mood states act as (hyper) priors over uncertainty (i.e. emotions). Here, we consider the same computational pathology in the proprioceptive and interoceptive (behavioural and autonomic) domain in order to furnish an explanation for mood disorders. This formulation reconciles several strands of research at multiple levels of enquiry.

Currently, there is an intense pursuit of pathognomonic markers and diagnostic (‘risk-based’) classifiers of psychiatric conditions. Commonly, the epidemiological prevalence of the condition is not factored into the development of these classifiers. By not adjusting for prevalence, classifiers overestimate the potential of their clinical utility. As valid predictive values have critical implications in public health and allocation of resources, development of clinical classifiers should account for the prevalence of psychiatric conditions in both general and high-risk populations. We suggest that classifiers are most likely to be useful when targeting enriched populations.

The main focus is on the assessment of the effects of early institutional care and compares three longitudinal studies from Romania, Greece and Hong Kong/China. The findings have been strikingly contrasting. The review asks if the risks are dependent on whether or not the institutional rearing is accompanied by gross pervasive deprivation (as it was in Romania) and investigates the methodological issues to explore the causal influence of the outcomes. Evidence is considered on changing institutional practices and the benefits of doing so. Comparison is made between institutions with major deprivation and those without global deprivation. A small number of studies are discussed that look at direct comparisons between institutional and community care. The empirical and conceptual implications of the findings are discussed.

Clinical trials in psychiatry inherit methods for design and statistical analysis from evidence-based medicine. However, trials in other clinical disciplines benefit from a more specific relationship between instruments that measure disease state (e.g. biomarkers, clinical signs), the underlying pathology and diagnosis such that primary outcomes can be readily defined. Trials in psychiatry use diagnosis (i.e. a categorical label for a syndrome) as a proxy for the underlying disorder, and outcomes are defined, for example, as a percentage change in a univariate total score on some clinical instrument. We label this approach to defining outcomes weak aggregation of disease state. Univariate measures are necessary, because statistical methodology is both tractable and well-developed for scalar outcomes, but we show that weak aggregate approaches do not capture disease state sufficiently, potentially leading to loss of information about response to intervention. We demonstrate how multivariate disease state can be captured using geometric concepts of spaces defined over routine clinical instruments, and show how clinically meaningful disease states (e.g. representing different profiles of symptoms, recovery or remission) can be defined as prototypes (geometric locations) in these spaces. Then, we show how to derive univariate (scalar) measures, which capture patient's relationships to these prototypes and argue these represent strong aggregates of disease state that may be a better basis for outcome measures. We demonstrate our proposal using a large publically available dataset. We conclude by discussing the impact of strong aggregates for analyses in traditional and novel trial designs.

Schizophrenia and other types of psychosis incur suffering, high health care costs and loss of human potential, due to the combination of early onset and poor response to treatment. Our ability to prevent or cure psychosis depends on knowledge of causal mechanisms. Molecular genetic studies show that thousands of common and rare variants contribute to the genetic risk for psychosis. Epidemiological studies have identified many environmental factors associated with increased risk of psychosis. However, no single genetic or environmental factor is sufficient to cause psychosis on its own. The risk of developing psychosis increases with the accumulation of many genetic risk variants and exposures to multiple adverse environmental factors. Additionally, the impact of environmental exposures likely depends on genetic factors, through gene–environment interactions. Only a few specific gene–environment combinations that lead to increased risk of psychosis have been identified to date. An example of replicable gene–environment interaction is a common polymorphism in the AKT1 gene that makes its carriers sensitive to developing psychosis with regular cannabis use. A synthesis of results from twin studies, molecular genetics, and epidemiological research outlines the many genetic and environmental factors contributing to psychosis. The interplay between these factors needs to be considered to draw a complete picture of etiology. To reach a more complete explanation of psychosis that can inform preventive strategies, future research should focus on longitudinal assessments of multiple environmental exposures within large, genotyped cohorts beginning early in life.

Fifty years have passed since social anxiety disorder (SAD) was first differentiated from other phobias. In the years since research has largely aligned with the zeitgeist of categorical classificatory frameworks, and has spanned identifying causes, maintenance factors and innovative interventions. Despite significant advances in the field, the capacity to conceptualise SAD as an independent entity is limited given the heterogeneity and dimensionality of diagnostic criteria, high rates of comorbidity, and non-specificity of aetiological mechanisms, maintaining factors and approaches to treatment. The Research Domain Criteria (RDoC) initiative was developed in an effort to overcome the inherent limitations posed by descriptive diagnostic systems – particularly in terms of reliability and validity – and in doing so seeks to facilitate research into underlying pathophysiological and behavioural mechanisms that cut across traditional diagnostic boundaries. The RDoC framework is furnished with a ‘matrix’, which in essence corresponds to a set of research principles that attempt to reconcile neuroscience and psychopathology. This review outlines a rationale for integrating SAD research with the RDoC approach, and offers examples of how future studies may wish to frame hypotheses and design experiments as the field moves towards classifying dimensions of psychopathology through a mechanistic understanding of underlying neurobiological and behavioural processes.