The question of whether psychopathology constructs are discrete kinds or continuous dimensions represents an important issue in clinical psychology and psychiatry. The present paper reviews psychometric modelling approaches that can be used to investigate this question through the application of statistical models. The relation between constructs and indicator variables in models with categorical and continuous latent variables is discussed, as are techniques specifically designed to address the distinction between latent categories as opposed to continua (taxometrics). In addition, we examine latent variable models that allow latent structures to have both continuous and categorical characteristics, such as factor mixture models and grade-of-membership models. Finally, we discuss recent alternative approaches based on network analysis and dynamical systems theory, which entail that the structure of constructs may be continuous for some individuals but categorical for others. Our evaluation of the psychometric literature shows that the kinds–continua distinction is considerably more subtle than is often presupposed in research; in particular, the hypotheses of kinds and continua are not mutually exclusive or exhaustive. We discuss opportunities to go beyond current research on the issue by using dynamical systems models, intra-individual time series and experimental manipulations.

Abnormal brain connectivity or network dysfunction has been suggested as a paradigm to understand several psychiatric disorders. We here review the use of novel meta-analytic approaches in neuroscience that go beyond a summary description of existing results by applying network analysis methods to previously published studies and/or publicly accessible databases. We define this strategy of combining connectivity with other brain characteristics as ‘meta-connectomics’. For example, we show how network analysis of task-based neuroimaging studies has been used to infer functional co-activation from primary data on regional activations. This approach has been able to relate cognition to functional network topology, demonstrating that the brain is composed of cognitively specialized functional subnetworks or modules, linked by a rich club of cognitively generalized regions that mediate many inter-modular connections. Another major application of meta-connectomics has been efforts to link meta-analytic maps of disorder-related abnormalities or MRI ‘lesions’ to the complex topology of the normative connectome. This work has highlighted the general importance of network hubs as hotspots for concentration of cortical grey-matter deficits in schizophrenia, Alzheimer's disease and other disorders. Finally, we show how by incorporating cellular and transcriptional data on individual nodes with network models of the connectome, studies have begun to elucidate the microscopic mechanisms underpinning the macroscopic organization of whole-brain networks. We argue that meta-connectomics is an exciting field, providing robust and integrative insights into brain organization that will likely play an important future role in consolidating network models of psychiatric disorders.

Psychiatric research has entered the age of ‘Big Data’. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other ‘omic’ measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may be further complicated by missing data for some subjects and variables that are highly correlated. Statistical learning-based models are a natural extension of classical statistical approaches but provide more effective methods to analyse very large datasets. In addition, the predictive capability of such models promises to be useful in developing decision support systems. That is, methods that can be introduced to clinical settings and guide, for example, diagnosis classification or personalized treatment. In this review, we aim to outline the potential benefits of statistical learning methods in clinical research. We first introduce the concept of Big Data in different environments. We then describe how modern statistical learning models can be used in practice on Big Datasets to extract relevant information. Finally, we discuss the strengths of using statistical learning in psychiatric studies, from both research and practical clinical points of view.

The DSM-I is currently viewed as a psychoanalytic classification, and therefore unimportant. There are four reasons to challenge the belief that DSM-I was a psychoanalytic system. First, psychoanalysts were a minority on the committee that created DSM-I. Second, psychoanalysts of the time did not use DSM-I. Third, DSM-I was as infused with Kraepelinian concepts as it was with psychoanalytic concepts. Fourth, contemporary writers who commented on DSM-I did not perceive it as psychoanalytic. The first edition of the DSM arose from a blending of concepts from the Statistical Manual for the Use of Hospitals of Mental Diseases, the military psychiatric classifications developed during World War II, and the International Classification of Diseases (6th edition). As a consensual, clinically oriented classification, DSM-I was popular, leading to 20 printings and international recognition. From the perspective inherent in this paper, the continuities between classifications from the first half of the 20th century and the systems developed in the second half (e.g. DSM-III to DSM-5) become more visible.

As the revision process leading to DSM-5 began, the domain of personality disorder embodied the highest aspirations for major change. After an initial prototype-based proposal failed to gain acceptance, the Personality and Personality Disorders Work Group (P&PDWG) developed a hybrid model containing categorical and dimensional components. A clash of perspectives both within the P&PDWG and between the P&PDWG and DSM-5 oversight committees led to the rejection of this proposal from the main body of DSM-5. Major issues included conflicting ways of conceptualizing validation, differences of opinion from personality disorder experts outside the P&PDWG, divergent concepts of the magnitude of evidence needed to support substantial changes, and the disagreements about clinical utility of the hybrid model. Despite these setbacks, the ‘Alternative DSM-5 Model of Personality Disorder’ is presented in Section III of the DSM-5. Further research should clarify its performance relative to the DSM-IV criteria reprinted in the main DSM-5 text.

There is evidence that 3.17% of women report post-traumatic stress disorder (PTSD) after childbirth. This meta-analysis synthesizes research on vulnerability and risk factors for birth-related PTSD and refines a diathesis–stress model of its aetiology. Systematic searches were carried out on PsycINFO, PubMed, Scopus and Web of Science using PTSD terms crossed with childbirth terms. Studies were included if they reported primary research that examined factors associated with birth-related PTSD measured at least 1 month after birth. In all, 50 studies (n = 21 429) from 15 countries fulfilled inclusion criteria. Pre-birth vulnerability factors most strongly associated with PTSD were depression in pregnancy (r = 0.51), fear of childbirth (r = 0.41), poor health or complications in pregnancy (r = 0.38), and a history of PTSD (r = 0.39) and counselling for pregnancy or birth (r = 0.32). Risk factors in birth most strongly associated with PTSD were negative subjective birth experiences (r = 0.59), having an operative birth (assisted vaginal or caesarean, r = 0.48), lack of support (r = −0.38) and dissociation (r = 0.32). After birth, PTSD was associated with poor coping and stress (r = 0.30), and was highly co-morbid with depression (r = 0.60). Moderator analyses showed that the effect of poor health or complications in pregnancy was more apparent in high-risk samples. The results of this meta-analysis are used to update a diathesis–stress model of the aetiology of postpartum PTSD and can be used to inform screening, prevention and intervention in maternity care.

Exercise can improve clinical outcomes in people with severe mental illness (SMI). However, this population typically engages in low levels of physical activity with poor adherence to exercise interventions. Understanding the motivating factors and barriers towards exercise for people with SMI would help to maximize exercise participation. A search of major electronic databases was conducted from inception until May 2016. Quantitative studies providing proportional data on the motivating factors and/or barriers towards exercise among patients with SMI were eligible. Random-effects meta-analyses were undertaken to calculate proportional data and 95% confidence intervals (CI) for motivating factors and barriers toward exercise. From 1468 studies, 12 independent studies of 6431 psychiatric patients were eligible for inclusion. Meta-analyses showed that 91% of people with SMI endorsed ‘improving health’ as a reason for exercise (N = 6, n = 790, 95% CI 80–94). Among specific aspects of health and well-being, the most common motivations were ‘losing weight’ (83% of patients), ‘improving mood’ (81%) and ‘reducing stress’ (78%). However, low mood and stress were also identified as the most prevalent barriers towards exercise (61% of patients), followed by ‘lack of support’ (50%). Many of the desirable outcomes of exercise for people with SMI, such as mood improvement, stress reduction and increased energy, are inversely related to the barriers of depression, stress and fatigue which frequently restrict their participation in exercise. Providing patients with professional support to identify and achieve their exercise goals may enable them to overcome psychological barriers, and maintain motivation towards regular physical activity.

Attention-deficit/hyperactivity disorder (ADHD) in childhood and adulthood is often treated with the psychostimulant methylphenidate (MPH). However, it is unknown whether cognitive effects of MPH depend on age in individuals with ADHD, while animal studies have suggested age-related effects. In this meta-analysis, we first determined the effects of MPH on response inhibition, working memory and sustained attention, but our main goal was to examine whether these effects are moderated by age. A systematic literature search using PubMed, PsycINFO, Web of Science and MEDLINE for double-blind, placebo-controlled studies with MPH resulted in 25 studies on response inhibition (n = 775), 13 studies on working memory (n = 559) and 29 studies on sustained attention (n = 956) (mean age range 4.8–50.1 years). The effects of MPH on response inhibition [effect size (ES) = 0.40, p < 0.0001, 95% confidence interval (CI) 0.22–0.58], working memory (ES = 0.24, p = 0.053, 95% CI 0.00–0.48) and sustained attention (ES = 0.42, p < 0.0001, 95% CI 26–0.59) were small to moderate. No linear or quadratic age-dependencies were observed, indicating that effects of MPH on executive functions are independent of age in children and adults with ADHD. However, adolescent studies are lacking and needed to conclude a lack of an age-dependency across the lifespan.

Common mental disorders (CMDs) are highly prevalent in the working population, and are associated with long-term sickness absence and disability. Workers on sick leave with CMDs would benefit from interventions that enable them to successfully return to work (RTW). However, the effectiveness of RTW interventions for workers with a CMD is not well studied. The objective of this review is to assess the effectiveness of existing workplace and clinical interventions that were aimed at enhancing RTW. A systematic review of studies of interventions for improving RTW in workers with a CMD was conducted. The main outcomes were proportion of RTW and sick-leave duration until RTW. Randomized controlled trials (RCTs) were identified from Medline/PubMed, PsycINFO, EMBASE, SocINDEX, and Human resource and management databases from January 1995 to 2016. Two authors independently selected studies, assessed risk of bias and extracted data. We pooled studies that we deemed sufficiently homogeneous in different comparison groups and assessed the overall quality of the evidence. We reviewed 2347 abstracts from which 136 full-text articles were reviewed and 16 RCTs were included in the analysis. Combined results from these studies suggested that the available interventions did not lead to improved RTW rates over the control group [pooled risk ratio 1.05, 95% confidence interval (CI) 0.97–1.12], but reduced the number of sick-leave days in the intervention group compared to the control group, with a mean difference of −13.38 days (95% CI −24.07 to −2.69).

Ecstasy/3,4-methylenedioxymethamphetamine (MDMA) use is proposed to cause damage to serotonergic (5-HT) axons in humans. Therefore, users should show deficits in cognitive processes that rely on serotonin-rich, prefrontal areas of the brain. However, there is inconsistency in findings to support this hypothesis. The aim of the current study was to examine deficits in executive functioning in ecstasy users compared with controls using meta-analysis. We identified k = 39 studies, contributing 89 effect sizes, investigating executive functioning in ecstasy users and polydrug-using controls. We compared function-specific task performance in 1221 current ecstasy users and 1242 drug-using controls, from tasks tapping the executive functions – updating, switching, inhibition and access to long-term memory. The significant main effect demonstrated overall executive dysfunction in ecstasy users [standardized mean difference (SMD) = −0.18, 95% confidence interval (CI) −0.26 to −0.11, Z = 5.05, p < 0.001, I2 = 82%], with a significant subgroup effect (χ2 = 22.06, degrees of freedom = 3, p < 0.001, I2 = 86.4%) demonstrating differential effects across executive functions. Ecstasy users showed significant performance deficits in access (SMD = −0.33, 95% CI −0.46 to −0.19, Z = 4.72, p < 0.001, I2 = 74%), switching (SMD = −0.19, 95% CI −0.36 to −0.02, Z = 2.16, p < 0.05, I2 = 85%) and updating (SMD = −0.26, 95% CI −0.37 to −0.15, Z = 4.49, p < 0.001, I2 = 82%). No differences were observed in inhibitory control. We conclude that this is the most comprehensive analysis of executive function in ecstasy users to date and provides a behavioural correlate of potential serotonergic neurotoxicity.

The development of DSM-III through DSM-5 has relied heavily on expert consensus. In this essay, we provide an historical and critical perspective on this process. Over the last 40 years, medicine has struggled to find appropriate methods for summarizing research results and making clinical recommendations. When such recommendations are issued by authorized organizations, they can have widespread influence (i.e. DSM-III and its successors). In the 1970s, expert consensus conferences, led by the NIH, reviewed research about controversial medical issues and successfully disseminated results. However, these consensus conferences struggled with aggregating the complex available evidence. In the 1990s, the rise of evidence-based medicine cast doubt on the reliability of expert consensus. Since then, medicine has increasingly relied on systematic reviews, as developed by the evidence-based medicine movement, and advocated for their early incorporation in expert consensus efforts. With the partial exception of DSM-IV, such systematic evidence-based reviews have not been consistently integrated into the development of the DSMs, leaving their development out of step with the larger medical field. Like the recommendations made for the NIH consensus conferences, we argue that the DSM process should be modified to require systematic evidence-based reviews before Work Groups make their assessments. Our suggestions – which would require leadership and additional resources to set standards for appropriate evidence hierarchies, carry out systematic reviews, and upgrade the group process – should improve the objectivity of the DSM, increase the validity of its results, and improve the reception of any changes in nosology.