Although bipolar disorder (BD) is a fundamentally cyclical illness, a divided model of BD that emphasizes polarity over cyclicity has dominated modern psychiatric diagnostic systems since their advent in the 1980s. However, there has been a gradual return to conceptualizations of BD which focus on longitudinal course in the research community due to emerging supportive data. Advances in longitudinal statistical methods promise to further progress the field.

The current study employed hidden Markov modeling to uncover empirically derived manic and depressive states from longitudinal data [i.e. Young Mania Rating Scale and Montgomery–Asberg Depression Rating Scale responses across five occasions from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) study], estimate participants’ probabilities of transitioning between these states over time (n = 3918), and evaluate whether clinical variables (e.g. rapid cycling and substance dependence) predict participants’ state transitions (n = 3229).

Analyses identified three empirically derived mood states (‘euthymic,’ ‘depressed,’ and ‘mixed’). Relative to the euthymic and depressed states, the mixed state was less commonly experienced, more temporally unstable, and uniquely associated with rapid cycling, substance use, and psychosis. Individuals assigned to the mixed state at baseline were relatively less likely to be diagnosed with BD-II (v. BD-I), more likely to present with a mixed or (hypo)manic episode, and reported experiencing irritable and elevated mood more frequently.

The results from the current study represent an important step in defining, and characterizing the longitudinal course of, empirically derived mood states that can be used to form the foundation of objective, empirical attempts to define meaningful subtypes of affective illness defined by clinical course.

The long-term outcomes of bipolar disorder range from lasting remission to chronic course or frequent recurrences requiring admissions. The distinction between bipolar I and II disorders has limited utility in outcome prediction. It is unclear to what extent the clinical course of bipolar disorder predicts long-term outcomes.

A representative sample of 191 individuals diagnosed with bipolar I or II disorder was recruited and followed for up to 5 years using a life-chart method. We previously described the clinical course over the first 18 months with dimensional course characteristics and latent classes. Now we test if these course characteristics predict long-term outcomes, including time ill (time with any mood symptoms) and hospital admissions over a second non-overlapping follow-up period in 111 individuals with available data from both 18 months and 5 years follow-ups.

Dimensional course characteristics from the first 18 months prospectively predicted outcomes over the following 3.5 years. The proportion of time depressed, the severity of depressive symptoms and the proportion of time manic predicted more time ill. The proportion of time manic, the severity of manic symptoms and depression-to-mania switching predicted a greater likelihood of hospital admissions. All predictions remained significant after controlling for age, sex and bipolar I v. II disorder.

Differential associations with long-term outcomes suggest that course characteristics may facilitate care planning with greater predictive validity than established types of bipolar disorders. A clinical course dominated by depressive symptoms predicts a greater proportion of time ill. A clinical course characterized by manic episodes predicts hospital admissions.

Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits.

Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics.

Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58).

A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.

The authors developed a practical and clinically useful model to predict the risk of psychosis that utilizes clinical characteristics empirically demonstrated to be strong predictors of conversion to psychosis in clinical high-risk (CHR) individuals. The model is based upon the Structured Interview for Psychosis Risk Syndromes (SIPS) and accompanying clinical interview, and yields scores indicating one's risk of conversion.

Baseline data, including demographic and clinical characteristics measured by the SIPS, were obtained on 199 CHR individuals seeking evaluation in the early detection and intervention for mental disorders program at the New York State Psychiatric Institute at Columbia University Medical Center. Each patient was followed for up to 2 years or until they developed a syndromal DSM-4 disorder. A LASSO logistic fitting procedure was used to construct a model for conversion specifically to a psychotic disorder.

At 2 years, 64 patients (32.2%) converted to a psychotic disorder. The top five variables with relatively large standardized effect sizes included SIPS subscales of visual perceptual abnormalities, dysphoric mood, unusual thought content, disorganized communication, and violent ideation. The concordance index (c-index) was 0.73, indicating a moderately strong ability to discriminate between converters and non-converters.

The prediction model performed well in classifying converters and non-converters and revealed SIPS measures that are relatively strong predictors of conversion, comparable with the risk calculator published by NAPLS (c-index = 0.71), but requiring only a structured clinical interview. Future work will seek to externally validate the model and enhance its performance with the incorporation of relevant biomarkers.

The effect of antipsychotics medication on cognitive functioning in patients diagnosed with schizophrenia is poorly understood. Some studies of second generation antipsychotics indicated that they improved cognitive functioning while other studies have found that they decrease the level of cognitive functioning.

We included patients with schizophrenia who were in treatment with antipsychotics 1.5 years (baseline) after initiation of treatment and followed them up 3.5 years later (n = 189). At follow-up 60 (32%) had discontinued their antipsychotic treatment and 129 (68%) were still taking antipsychotics. Using the Brief Assessment of Cognition in Schizophrenia (BACS) we assessed cognition at baseline and follow-up.

The patients who discontinued their medication had a higher level of cognitive functioning in all domains at baseline, as well as Global cognitive function [mean z-score −1.50 (s.d. 1.24) v. −2.27 (s.d. 1.30), p = 0.00015]. After controlling for relevant confounders those who discontinued antipsychotic medication improved significantly more than those who remained on antipsychotic medication during the course of the follow-up on the Token Motor task [estimated mean change difference −0.46 (95% CI −0.89 to −0.04)], the Speed of Processing Domain [estimated mean change difference −0.38 (95% CI −0.68 to −0.08)] and global cognition [estimated mean change difference −0.36 (95% CI −0.66 to −0.07)].

Due to the naturalistic design, we cannot conclude on the direction of the relationship between antipsychotics and cognition. There is no evidence that discontinuation of medication had a negative effect on cognitive functioning. Rather, we found that that discontinuation of medication was associated with better cognitive functioning.

Based on the vulnerability–stress model, we aimed to (1) determine new onset of depression in individuals who had not shown evidence of depression at baseline (5 years earlier) and (2) identify social, psychological, behavioral, and somatic predictors.

Longitudinal data of N = 10 036 participants (40–79 years) were evaluated who had no evidence of depression at baseline based on Patient Health Questionnaire (PHQ-9), no history of depression, or intake of antidepressants. Multivariate logistic regression models were used to predict the onset of depression.

Prevalence of new cases of depression was 4.4%. Higher rates of women (5.1%) than men (3.8%) were due to their excess incidence <60 years of age. Regression analyses revealed significant social, psychological, behavioral, and somatic predictors: loneliness [odds ratio (OR) 2.01; 95% confidence interval (CI) 1.48–2.71], generalized anxiety (OR 2.65; 1.79–3.85), social phobia (OR 1.87; 1.34–2.57), panic (OR 1.67; 1.01–2.64), type D personality (OR 1.85; 1.47–2.32), smoking (OR 1.35; 1.05–1.71), and comorbid cancer (OR 1.58; 1.09–2.24). Protective factors were age (OR 0.88; 0.83–0.93) and social support (OR 0.93; 0.90–0.95). Stratified by sex, cancer was predictive for women; for men smoking and life events. Entered additionally, the PHQ-9 baseline score was strongly predictive (OR 1.40; 1.34–1.47), generalized anxiety became only marginally, and panic was no longer predictive. Other predictors remained significant, albeit weaker.

Psychobiological vulnerability, stress, and illness-related factors were predictive of new onset of depression, whereas social support was protective. Baseline subclinical depression was an additional risk weakening the relationship between anxiety and depression by taking their overlap into account. Vulnerability factors differed between men and women.

The role of the cerebellum in obsessive-compulsive disorder (OCD) has drawn increasing attention. However, the functional connectivity between the cerebellum and the cerebral cortex has not been investigated in OCD, nor has the relationship between such functional connectivity and clinical symptoms.

A total of 27 patients with OCD and 21 healthy controls (HCs) matched on age, sex and education underwent magnetic resonance imaging (MRI). Seed-based connectivity analyses were performed to examine differences in cerebellar-cerebral connectivity in patients with OCD compared with HCs. Associations between functional connectivity and clinical features in OCD were analyzed.

Compared with HCs, OCD patients showed significantly decreased cerebellar-cerebral functional connectivity in executive control and emotion processing networks. Within the OCD group, decreased functional connectivity in an executive network spanning the right cerebellar Crus I and the inferior parietal lobule was positively correlated with symptom severity, and decreased connectivity in an emotion processing network spanning the left cerebellar lobule VI and the lingual gyrus was negatively correlated with illness duration.

Altered functional connectivity between the cerebellum and cerebral networks involved in cognitive-affective processing in patients with OCD provides further evidence for the involvement of the cerebellum in the pathophysiology of OCD, and is consistent with impairment in executive control and emotion regulation in this condition.

Most studies underline the contribution of heritable factors for psychiatric disorders. However, heritability estimates depend on the population under study, diagnostic instruments, and study designs that each has its inherent assumptions, strengths, and biases. We aim to test the homogeneity in heritability estimates between two powerful, and state of the art study designs for eight psychiatric disorders.

We assessed heritability based on data of Swedish siblings (N = 4 408 646 full and maternal half-siblings), and based on summary data of eight samples with measured genotypes (N = 125 533 cases and 208 215 controls). All data were based on standard diagnostic criteria. Eight psychiatric disorders were studied: (1) alcohol dependence (AD), (2) anorexia nervosa, (3) attention deficit/hyperactivity disorder (ADHD), (4) autism spectrum disorder, (5) bipolar disorder, (6) major depressive disorder, (7) obsessive-compulsive disorder (OCD), and (8) schizophrenia.

Heritability estimates from sibling data varied from 0.30 for Major Depression to 0.80 for ADHD. The estimates based on the measured genotypes were lower, ranging from 0.10 for AD to 0.28 for OCD, but were significant, and correlated positively (0.19) with national sibling-based estimates. When removing OCD from the data the correlation increased to 0.50.

Given the unique character of each study design, the convergent findings for these eight psychiatric conditions suggest that heritability estimates are robust across different methods. The findings also highlight large differences in genetic and environmental influences between psychiatric disorders, providing future directions for etiological psychiatric research.

Chronic opioid exposure is common world-wide, but behavioural performance remains under-investigated. This study aimed to investigate visuospatial memory performance in opioid-exposed and dependent clinical populations and its associations with measures of intelligence and cognitive impulsivity.

We recruited 109 participants: (i) patients with a history of opioid dependence due to chronic heroin use (n = 24), (ii) heroin users stabilised on methadone maintenance treatment (n = 29), (iii) participants with a history of chronic pain and prescribed tramadol and codeine (n = 28) and (iv) healthy controls (n = 28). The neuropsychological tasks from the Cambridge Neuropsychological Test Automated Battery included the Delayed Matching to Sample (DMS), Pattern Recognition Memory, Spatial Recognition Memory, Paired Associate Learning, Spatial Span Task, Spatial Working Memory and Cambridge Gambling Task. Pre-morbid general intelligence was assessed using the National Adult Reading Test.

As hypothesised, this study identified the differential effects of chronic heroin and methadone exposures on neuropsychological measures of visuospatial memory (p < 0.01) that were independent of injecting behaviour and dependence status. The study also identified an improvement in DMS performance (specifically at longer delays) when the methadone group was compared with the heroin group and also when the heroin group was stabilised onto methadone. Results identified differential effects of chronic heroin and methadone exposures on various neuropsychological measures of visuospatial memory independently from addiction severity measures, such as injecting behaviour and dependence status.

Attention-deficit/hyperactivity disorder (ADHD) is a developmental condition that profoundly affects quality of life. Although mounting evidence now suggests uncontrolled mind-wandering as a core aspect of the attentional problems associated with ADHD, the neural mechanisms underpinning this deficit remains unclear. To that extent, competing views argue for (i) excessive generation of task-unrelated mental content, or (ii) deficiency in the control of task-relevant cognition.

In a cross-sectional investigation of a large neurotypical cohort (n = 184), we examined alterations in the intrinsic brain functional connectivity architecture of the default mode (DMN) and frontoparietal (FPN) networks during resting state functional magnetic resonance imaging in relation to ADHD symptomatology, which could potentially underlie changes in ongoing thought within variable environmental contexts.

The results illustrated that ADHD symptoms were linked to lower levels of detail in ongoing thought while the participants made more difficult, memory based decisions. Moreover, greater ADHD scores were associated with lower levels of connectivity between the DMN and right sensorimotor cortex, and between the FPN and right ventral visual cortex. Finally, a combination of high levels of ADHD symptomology with reduced FPN connectivity to the visual cortex was associated with reduced levels of detail in thought.

The results of our study suggest that the frequent mind-wandering observed in ADHD may be an indirect consequence of the deficient control of ongoing cognition in response to increasing environmental demands, and that this may partly arise from dysfunctions in the intrinsic organisation of the FPN at rest.

Mismatch negativity (MMN) is an event-related potential (ERP) component reflecting auditory predictive coding. Repeated standard tones evoke increasing positivity (‘repetition positivity’; RP), reflecting strengthening of the standard's memory trace and the prediction it will recur. Likewise, deviant tones preceded by more standard repetitions evoke greater negativity (‘deviant negativity’; DN), reflecting stronger prediction error signaling. These memory trace effects are also evident in MMN difference wave. Here, we assess group differences and test-retest reliability of these indices in schizophrenia patients (SZ) and healthy controls (HC).

Electroencephalography was recorded twice, 2 weeks apart, from 43 SZ and 30 HC, during a roving standard paradigm. We examined ERPs to the third, eighth, and 33rd standards (RP), immediately subsequent deviants (DN), and the corresponding MMN. Memory trace effects were assessed by comparing amplitudes associated with the three standard repetition trains.

Compared with controls, SZ showed reduced MMNs and DNs, but normal RPs. Both groups showed memory trace effects for RP, MMN, and DN, with a trend for attenuated DNs in SZ. Intraclass correlations obtained via this paradigm indicated good-to-moderate reliabilities for overall MMN, DN and RP, but moderate to poor reliabilities for components associated with short, intermediate, and long standard trains, and poor reliability of their memory trace effects.

MMN deficits in SZ reflected attenuated prediction error signaling (DN), with relatively intact predictive code formation (RP) and memory trace effects. This roving standard MMN paradigm requires additional development/validation to obtain suitable levels of reliability for use in clinical trials.

Increased neural error-signals have been observed in obsessive-compulsive disorder (OCD), anxiety disorders, and inconsistently in depression. Reduced neural error-signals have been observed in substance use disorders (SUD). Thus, alterations in error-monitoring are proposed as a transdiagnostic endophenotype. To strengthen this notion, data from unaffected individuals with a family history for the respective disorders are needed.

The error-related negativity (ERN) as a neural indicator of error-monitoring was measured during a flanker task from 117 OCD patients, 50 unaffected first-degree relatives of OCD patients, and 130 healthy comparison participants. Family history information indicated, that 76 healthy controls were free of a family history for psychopathology, whereas the remaining had first-degree relatives with depression (n = 28), anxiety (n = 27), and/or SUD (n = 27).

Increased ERN amplitudes were found in OCD patients and unaffected first-degree relatives of OCD patients. In addition, unaffected first-degree relatives of individuals with anxiety disorders were also characterized by increased ERN amplitudes, whereas relatives of individuals with SUD showed reduced amplitudes.

Alterations in neural error-signals in unaffected first-degree relatives with a family history of OCD, anxiety, or SUD support the utility of the ERN as a transdiagnostic endophenotype. Reduced neural error-signals may indicate vulnerability for under-controlled behavior and risk for substance use, whereas a harm- or error-avoidant response style and vulnerability for OCD and anxiety appears to be associated with increased ERN. This adds to findings suggesting a common neurobiological substrate across psychiatric disorders involving the anterior cingulate cortex and deficits in cognitive control.

Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC.

Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals).

Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation.

This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts.