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Dimensional models of co-morbidity have the potential to improve the conceptualization of mental disorders in research and clinical work, yet little is known about how relatively uncommon disorders may fit with more common disorders. The present study estimated the meta-structure of psychopathology in the US general population focusing on the placement of five under-studied disorders sharing features of thought disorder: paranoid, schizoid, avoidant and schizotypal personality disorders, and manic episodes as well as bipolar disorder. Data were drawn from the National Epidemiologic Survey on Alcohol and Related Conditions, a face-to-face interview of 34 653 non-institutionalized adults in the US general population. The meta-structure of 16 DSM-IV Axis I and Axis II psychiatric disorders, as assessed by the Alcohol Use Disorder and Associated Disabilities Interview Schedule DSM-IV version (AUDADIS-IV), was examined using exploratory and confirmatory factor analysis. We document an empirically derived thought disorder factor that is a subdomain of the internalizing dimension, characterized by schizoid, paranoid, schizotypal and avoidant personality disorders as well as manic episodes. Manic episodes exhibit notable associations with both the distress subdomain of the internalizing dimension as well as the thought disorder subdomain. The structure was replicated for bipolar disorder (I or II) in place of manic episodes. As our understanding of psychopathological meta-structure expands, incorporation of disorders characterized by detachment and psychoticism grows increasingly important. Disorders characterized by detachment and psychoticism may be well conceptualized, organized and measured as a subdimension of the internalizing spectrum of disorders. Manic episodes and bipolar disorder exhibit substantial co-morbidity across both distress and thought disorder domains of the internalizing dimension. Clinically, these results underscore the potential utility of conceptualizing patient treatment needs using an approach targeting psychopathological systems underlying meta-structural classification rubrics.

This post-hoc analysis assessed rates of symptomatic and functional remission, as well as recovery (combination of symptomatic and functional remission), in patients treated with lurasidone for major depressive disorder (MDD) associated with subthreshold hypomanic symptoms (mixed features). Patients with MDD plus two or three manic symptoms (defined as per the DSM-5 mixed-features specifier) were randomly assigned to flexible-dose lurasidone 20-60 mg/day (n=109) or placebo (n=100) for 6 weeks, followed by a 3-month open-label, flexible-dose extension study for U.S. sites only (n=48). Cross-sectional recovery was defined as the presence of both symptomatic remission (Montgomery-Åsberg Depression Rating Scale score ≤ 12) and functional remission (all Sheehan Disability Scale [SDS] domain scores ≤3) at week 6, and at both months 1 and 3 of the extension study (\"sustained recovery\"). A significantly higher proportion of lurasidone-treated patients (31.3%) achieved recovery (assessed cross-sectionally) compared to placebo (12.2%, p=0.002) at week 6. The number of manic symptoms at baseline moderated the effect size for attaining cross-sectional recovery for lurasidone treatment (vs. placebo) (p=0.028). Sustained recovery rates were higher in patients initially treated with lurasidone (20.8%) versus placebo (12.5%). In this post-hoc analysis of a placebo-controlled study with open-label extension that involved patients with MDD and mixed features, lurasidone was found to significantly improve the rate of recovery at 6 weeks (vs. placebo) that was sustained at month 3 of the extension study. The presence of two (as opposed to three) manic symptoms moderated recovery at the acute study endpoint.

Objective : To compare the prevalence of Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) mania symptoms in a prepubertal and early adolescent bipolar disorder phenotype(PEA-BP) to those with attention deficit hyperactivity disorder (ADHD) and normal community controls (CC). Methods : To optimize generalizeability, subjects with PEA-BP and ADHD were consecutively ascertained from outpatient pediatric and psychiatric sites, and CC subjects were obtained from a random survey. All 268 subjects (93 with PEA-BP, 81 with ADHD, and 94 CC) received comprehensive, blind, baseline research assessments of mothers about their children and of children about themselves. PEA-BP was defined by DSM-IV mania with elation and/or grandiosity as one criterion to ensure that subjects had one of the two cardinal symptoms of mania and to avoid diagnosing mania only by criteria that overlapped with those for ADHD. Results : Five symptoms (i.e., elation, grandiosity, flight of ideas/racing thoughts, decreased need for sleep, and hypersexuality) provided the best discrimination of PEA-BP subjects from ADHD and CC controls. These five symptoms are also mania-specific in DSM-IV (i.e., they do not overlap with DSM-IV symptoms for ADHD). Irritability, hyperactivity, accelerated speech, and distractibility were very frequent in both PEA-BP and ADHD groups and therefore were not useful for differential diagnosis. Concurrent elation and irritability occurred in 87.1% of subjects with PEA-BP. Data on suicidality, psychosis, mixed mania, and continuous rapid cycling were also provided. Conclusion : Unlike late teenage/adult onset bipolar disorder, even subjects with PEA-BP selected for DSM-IV mania with cardinal symptoms have high rates of comorbid DSM-IV ADHD. High rates of concurrent elation and irritability were similar to those in adult mania.

Bipolar disorders are a complex group of severe and chronic disorders that includes bipolar I disorder, defined by the presence of a syndromal, manic episode, and bipolar II disorder, defined by the presence of a syndromal, hypomanic episode and a major depressive episode. Bipolar disorders substantially reduce psychosocial functioning and are associated with a loss of approximately 10–20 potential years of life. The mortality gap between populations with bipolar disorders and the general population is principally a result of excess deaths from cardiovascular disease and suicide. Bipolar disorder has a high heritability (approximately 70%). Bipolar disorders share genetic risk alleles with other mental and medical disorders. Bipolar I has a closer genetic association with schizophrenia relative to bipolar II, which has a closer genetic association with major depressive disorder. Although the pathogenesis of bipolar disorders is unknown, implicated processes include disturbances in neuronal-glial plasticity, monoaminergic signalling, inflammatory homoeostasis, cellular metabolic pathways, and mitochondrial function. The high prevalence of childhood maltreatment in people with bipolar disorders and the association between childhood maltreatment and a more complex presentation of bipolar disorder (eg, one including suicidality) highlight the role of adverse environmental exposures on the presentation of bipolar disorders. Although mania defines bipolar I disorder, depressive episodes and symptoms dominate the longitudinal course of, and disproportionately account for morbidity and mortality in, bipolar disorders. Lithium is the gold standard mood-stabilising agent for the treatment of people with bipolar disorders, and has antimanic, antidepressant, and anti-suicide effects. Although antipsychotics are effective in treating mania, few antipsychotics have proven to be effective in bipolar depression. Divalproex and carbamazepine are effective in the treatment of acute mania and lamotrigine is effective at treating and preventing bipolar depression. Antidepressants are widely prescribed for bipolar disorders despite a paucity of compelling evidence for their short-term or long-term efficacy. Moreover, antidepressant prescription in bipolar disorder is associated, in many cases, with mood destabilisation, especially during maintenance treatment. Unfortunately, effective pharmacological treatments for bipolar disorders are not universally available, particularly in low-income and middle-income countries. Targeting medical and psychiatric comorbidity, integrating adjunctive psychosocial treatments, and involving caregivers have been shown to improve health outcomes for people with bipolar disorders. The aim of this Seminar, which is intended mainly for primary care physicians, is to provide an overview of diagnostic, pathogenetic, and treatment considerations in bipolar disorders. Towards the foregoing aim, we review and synthesise evidence on the epidemiology, mechanisms, screening, and treatment of bipolar disorders.

Bipolar disorder is a recurrent chronic disorder characterised by fluctuations in mood state and energy. It affects more than 1% of the world's population irrespective of nationality, ethnic origin, or socioeconomic status. Bipolar disorder is one of the main causes of disability among young people, leading to cognitive and functional impairment and raised mortality, particularly death by suicide. A high prevalence of psychiatric and medical comorbidities is typical in affected individuals. Accurate diagnosis of bipolar disorder is difficult in clinical practice because onset is most commonly a depressive episode and looks similar to unipolar depression. Moreover, there are currently no valid biomarkers for the disorder. Therefore, the role of clinical assessment remains key. Detection of hypomanic periods and longitudinal assessment are crucial to differentiate bipolar disorder from other conditions. Current knowledge of the evolving pharmacological and psychological strategies in bipolar disorder is of utmost importance.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P  < 1 × 10 −4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant ( P  < 5 × 10 −8 ) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder. Genome-wide analysis identifies 30 loci associated with bipolar disorder, allowing for comparisons of shared genes and pathways with other psychiatric disorders, including schizophrenia and depression.

Recently, functional network connectivity (FNC, defined as the temporal correlation among spatially distant brain networks) has been used to examine the functional organization of brain networks in various psychiatric illnesses. Dynamic FNC is a recent extension of the conventional FNC analysis that takes into account FNC changes over short periods of time. While such dynamic FNC measures may be more informative about various aspects of connectivity, there has been no detailed head-to-head comparison of the ability of static and dynamic FNC to perform classification in complex mental illnesses. This paper proposes a framework for automatic classification of schizophrenia, bipolar and healthy subjects based on their static and dynamic FNC features. Also, we compare cross-validated classification performance between static and dynamic FNC. Results show that the dynamic FNC significantly outperforms the static FNC in terms of predictive accuracy, indicating that features from dynamic FNC have distinct advantages over static FNC for classification purposes. Moreover, combining static and dynamic FNC features does not significantly improve the classification performance over the dynamic FNC features alone, suggesting that static FNC does not add any significant information when combined with dynamic FNC for classification purposes. A three-way classification methodology based on static and dynamic FNC features discriminates individual subjects into appropriate diagnostic groups with high accuracy. Our proposed classification framework is potentially applicable to additional mental disorders. •Performed classification using static and dynamic connectivity features in schizophrenia and bipolar disorder during rest.•Classification using connectivity features discriminates subjects into appropriate diagnostic groups with high accuracy.•Classification using dynamic connectivity features has significantly higher predictive accuracy than static FNC.•Combining both connectivity features does not add significant information for classification purposes.

A significant minority of people presenting with a major depressive episode (MDE) experience co-occurring subsyndromal hypo/manic symptoms. As this presentation may have important prognostic and treatment implications, the DSM–5 codified a new nosological entity, the “mixed features specifier,” referring to individuals meeting threshold criteria for an MDE and subthreshold symptoms of (hypo)mania or to individuals with syndromal mania and subthreshold depressive symptoms. The mixed features specifier adds to a growing list of monikers that have been put forward to describe phenotypes characterized by the admixture of depressive and hypomanic symptoms (e.g., mixed depression, depression with mixed features, or depressive mixed states [DMX]). Current treatment guidelines, regulatory approvals, as well the current evidentiary base provide insufficient decision support to practitioners who provide care to individuals presenting with an MDE with mixed features. In addition, all existing psychotropic agents evaluated in mixed patients have largely been confined to patient populations meeting the DSM–IV definition of “mixed states” wherein the co-occurrence of threshold-level mania and threshold-level MDE was required. Toward the aim of assisting clinicians providing care to adults with MDE and mixed features, we have assembled a panel of experts on mood disorders to develop these guidelines on the recognition and treatment of mixed depression, based on the few studies that have focused specifically on DMX as well as decades of cumulated clinical experience.

Recent data suggest trait-like neurocognitive impairments in bipolar disorder (BPD), with deficits about 1 s.d. below average, less severe than deficits noted in schizophrenia. The frequency of significant impairment in BPD is approximately 60%, with 40% of patients characterized as cognitively spared. This contrasts with a more homogeneous presentation in schizophrenia. It is not understood why some BPD patients develop deficits while others do not. A total of 136 patients with BPD completed the MATRICS Consensus Cognitive Battery and data were entered into hierarchical cluster analyses to: (1) determine the optimal number of clusters (subgroups) that fit the sample; and (2) assign subjects to a specific cluster based on individual profiles. We then compared subgroups on several clinical factors and real-world community functioning. Three distinct neurocognitive subgroups were found: (1) an intact group with performance comparable with healthy controls on all domains but with superior social cognition; (2) a selective impairment group with moderate deficits on processing speed, attention, verbal learning and social cognition and normal functioning in other domains; and (3) a global impairment group with severe deficits across all cognitive domains comparable with deficits in schizophrenia. These results suggest the presence of multiple cognitive subgroups in BPD with unique profiles and begin to address the relationships between these subgroups, several clinical factors and functional outcome. Next steps will include using these data to help guide future efforts to target these disabling symptoms with treatment.

Current group-average analysis suggests quantitative but not qualitative cognitive differences between schizophrenia (SZ) and bipolar disorder (BD). There is increasing recognition that cognitive within-group heterogeneity exists in both disorders, but it remains unclear as to whether between-group comparisons of performance in cognitive subgroups emerging from within each of these nosological categories uphold group-average findings. We addressed this by identifying cognitive subgroups in large samples of SZ and BD patients independently, and comparing their cognitive profiles. The utility of a cross-diagnostic clustering approach to understanding cognitive heterogeneity in these patients was also explored. Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575). Three qualitatively and quantitatively similar clusters emerged within each clinical group: a severely impaired cluster, a mild-moderately impaired cluster and a relatively intact cognitive cluster. A cross-diagnostic clustering solution also resulted in three subgroups and was superior in reducing cognitive heterogeneity compared with disorder clustering independently. Quantitative SZ-BD cognitive differences commonly seen using group averages did not hold when cognitive heterogeneity was factored into our sample. Members of each corresponding subgroup, irrespective of diagnosis, might be manifesting the outcome of differences in shared cognitive risk factors.