Convergent Evidence from Animal and Human Studies

Schizophrenia is a highly heritable disorder with diverse mental and somatic symptoms. The molecular mechanisms leading from genes to disease pathology in schizophrenia remain largely unknown. Genome-wide association studies (GWASs) have shown that common single-nucleotide polymorphisms associated with specific diseases are enriched in the recognition sequences of transcription factors that regulate physiological processes relevant to the disease. We have used a “bottom-up” approach and tracked a developmental trajectory from embryology to physiological processes and behavior and recognized that the transcription factor NK2 homeobox 1 (NKX2-1) possesses properties of particular interest for schizophrenia. NKX2-1 is selectively expressed from prenatal development to adulthood in the brain, thyroid gland, parathyroid gland, lungs, skin, and enteric ganglia, and has key functions at the interface of the brain, the endocrine-, and the immune system. In the developing brain, NKX2-1-expressing progenitor cells differentiate into distinct subclasses of forebrain GABAergic and cholinergic neurons, astrocytes, and oligodendrocytes. The transcription factor is highly expressed in mature limbic circuits related to context-dependent goal-directed patterns of behavior, social interaction and reproduction, fear responses, responses to light, and other homeostatic processes. It is essential for development and mature function of the thyroid gland and the respiratory system, and is involved in calcium metabolism and immune responses. NKX2-1 interacts with a number of genes identified as susceptibility genes for schizophrenia. We suggest that NKX2-1 may lie at the core of several dose dependent pathways that are dysregulated in schizophrenia. We correlate the symptoms seen in schizophrenia with the temporal and spatial activities of NKX2-1 in order to highlight promising future research areas

Motor function may differentiate attention deficit hyperactivity disorder from early onset bipolar disorder

<p>Abstract</p> <p>Background</p> <p>Differentiating between bipolar spectrum disorder (BD) and attention deficit hyperactivity disorder (ADHD) in childhood and adolescence is difficult because the clinical presentation is influenced by ongoing neural development, causing considerable symptom overlap. Motor problems and neurological soft signs have been associated with ADHD for decades. Little is known about motor skills in BD. Here we assess the diagnostic accuracy of neuromotor deviations in differentiating ADHD from BD in clinical practice. We also investigate if these deviations exist in concurrent ADHD and BD, thus indicating true comorbidity</p> <p>Methods</p> <p>64 patients 6-18 years (31 girls, 33 boys) fulfilling the diagnostic criteria of BD, ADHD combined subtype (ADHD-C) or comorbid BD and ADHD-C, were compared using an age-standardized neuromotor test; NUBU. Categorical variables were analyzed using cross table with two-tailed chi square test or Fisher's exact test when appropriate. Continuous variables were analyzed by Kruskal-Wallis test and, if significant, Mann-Whitney U test and ROC plots.</p> <p>Results</p> <p>The ADHD-C group and the comorbid ADHD-C and BD group both showed significantly more neurological soft signs (p less than 0.01) and lower mean static coordination percentile (p less than 0.01) than the BD group. The positive predictive value of NUBU in the diagnosis of ADHD-C with or without concurrent BD was 89% (80-95) for total soft signs and 87% (79-95) for static coordination below the 7.5 percentile.</p> <p>Conclusion</p> <p>An age-standardized neuromotor test battery may promote diagnostic accuracy in differentiating ADHD from BD in clinical practice, and help evaluating whether symptoms of ADHD in children who have BD reflect symptom overlap or real comorbidity. This may have important implications for everyday diagnostic work.</p

The study protocol of the Norwegian randomized controlled trial of electroconvulsive therapy in treatment resistant depression in bipolar disorder

<p>Abstract</p> <p>Background</p> <p>The treatment of depressive phases of bipolar disorder is challenging. The effects of the commonly used antidepressants in bipolar depression are questionable. Electroconvulsive therapy is generally considered to be the most effective treatment even if there are no randomized controlled trials of electroconvulsive therapy in bipolar depression. The safety of electroconvulsive therapy is well documented, but there are some controversies as to the cognitive side effects. The aim of this study is to compare the effects and side effects of electroconvulsive therapy to pharmacological treatment in treatment resistant bipolar depression. Cognitive changes and quality of life during the treatment will be assessed.</p> <p>Methods/Design</p> <p>A prospective, randomised controlled, multi-centre six- week acute treatment trial with seven clinical assessments. Follow up visit at 26 weeks or until remission (max 52 weeks). A neuropsychological test battery designed to be sensitive to changes in cognitive function will be used. Setting: Nine study centres across Norway, all acute psychiatric departments. Sample: n = 132 patients, aged 18 and over, who fulfil criteria for treatment resistant depression in bipolar disorder, Montgomery Åsberg Depression Rating Scale Score of at least 25 at baseline. Intervention: Intervention group: 3 sessions per week for up to 6 weeks, total up to 18 sessions. Control group: algorithm-based pharmacological treatment as usual.</p> <p>Discussion</p> <p>This study is the first randomized controlled trial that aims to investigate whether electroconvulsive therapy is better than pharmacological treatment as usual in treatment resistant bipolar depression. Possible long lasting cognitive side effects will be evaluated. The study is investigator initiated, without support from industry.</p> <p>Trial registration</p> <p>NCT00664976</p

Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals

AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD

A European research agenda for somatic symptom disorders, bodily distress disorders, and functional disorders: Results of an estimate-talk-estimate delphi expert study

Background: Somatic Symptom Disorders (SSD), Bodily Distress Disorders (BDD) and functional disorders (FD) are associated with high medical and societal costs and pose a substantial challenge to the population and health policy of Europe. To meet this challenge, a specific research agenda is needed as one of the cornerstones of sustainable mental health research and health policy for SSD, BDD, and FD in Europe. Aim: To identify the main challenges and research priorities concerning SSD, BDD, and FD from a European perspective. Methods: Delphi study conducted from July 2016 until October 2017 in 3 rounds with 3 workshop meetings and 3 online surveys, involving 75 experts and 21 European countries. EURONET-SOMA and the European Association of Psychosomatic Medicine (EAPM) hosted the meetings. Results: Eight research priorities were identified: (1) Assessment of diagnostic profiles relevant to course and treatment outcome. (2) Development and evaluation of new, effective interventions. (3) Validation studies on questionnaires or semi-structured interviews that assess chronic medical conditions in this context. (4) Research into patients preferences for diagnosis and treatment. (5) Development of new methodologic designs to identify and explore mediators and moderators of clinical course and treatment outcomes (6). Translational research exploring how psychological and somatic symptoms develop from somatic conditions and biological and behavioral pathogenic factors. (7) Development of new, effective interventions to personalize treatment. (8) Implementation studies of treatment interventions in different settings, such as primary care, occupational care, general hospital and specialty mental health settings. The general public and policymakers will benefit from the development of new, effective, personalized interventions for SSD, BDD, and FD, that will be enhanced by translational research, as well as from the outcomes of research into patient involvement, GP-patient communication, consultation-liaison models and implementation. Conclusion: Funding for this research agenda, targeting these challenges in coordinated research networks such as EURONET-SOMA and EAPM, and systematically allocating resources by policymakers to this critical area in mental and physical well-being is urgently needed to improve efficacy and impact for diagnosis and treatment of SSD, BDD, and FD across Europe

Comorbid mental disorders in substance users from a single catchment area - a clinical study

<p>Abstract</p> <p>Background</p> <p>The optimal treatment of patients with substance use disorders (SUDs) requires an awareness of their comorbid mental disorders and vice versa. The prevalence of comorbidity in first-time-admitted SUD patients has been insufficiently studied. Diagnosing comorbidity in substance users is complicated by symptom overlap, symptom fluctuations, and the limitations of the assessment methods. The aim of this study was to diagnose all mental disorders in substance users living in a single catchment area, without any history of treatment for addiction or psychiatric disorders, admitted consecutively to the specialist health services. The prevalence of substance-induced versus substance-independent disorders according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), in SUD patients will be described.</p> <p>Methods</p> <p>First-time consecutively admitted patients from a single catchment area, aged 16 years or older, admitted to addiction clinics or departments of psychiatry as outpatients or inpatients will be screened for substance-related problems using the Alcohol Use Disorder Identification Test and the Drug Use Disorder Identification Test. All patients with scores above the cutoff value will be asked to participate in the study. The patients included will be diagnosed for SUD and other axis I disorders by a psychiatrist using the Psychiatric Research Interview for Substance and Mental Disorders. This interview was designed for the diagnosis of primary and substance-induced disorders in substance users. Personality disorders will be assessed according to the Structured Clinical Interview for DSM-IV axis II disorders. The Symptom Checklist-90-Revised, the Inventory of Depressive Symptoms, the Montgomery Asberg Depression Rating Scale, the Young Mania Rating Scale, and the Angst Hypomania Check List will be used for additional diagnostic assessments. The sociodemographic data will be recorded with the Stanley Foundation's Network Entry Questionnaire. Biochemical assessments will reveal somatic diseases that may contribute to the patient's symptoms.</p> <p>Discussion</p> <p>This study is unique because the material represents a complete sample of first-time-admitted treatment seekers with SUD from a single catchment area. Earlier studies have not focused on first-time-admitted patients, so chronically ill patients, may have been overrepresented in those samples. This study will contribute new knowledge about mental disorders in first-time-admitted SUD patients.</p

In vivo hippocampal subfield volumes in bipolar disorder—A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta‐Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1‐weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed‐effects models and mega‐analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = −0.20), cornu ammonis (CA)1 (d = −0.18), CA2/3 (d = −0.11), CA4 (d = −0.19), molecular layer (d = −0.21), granule cell layer of dentate gyrus (d = −0.21), hippocampal tail (d = −0.10), subiculum (d = −0.15), presubiculum (d = −0.18), and hippocampal amygdala transition area (d = −0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non‐users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

publisher: Elsevier articletitle: Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes journaltitle: Cell articlelink: https://doi.org/10.1016/j.cell.2018.05.046 content_type: article copyright: © 2018 Elsevier Inc