Progress in research at the cibersam's Affective disorders programme of the University of Barcelona Hospital Clinic.

The affective disorders programme at the University of Barcelona Hospital Clinic involves two separate subgroups according to their research target: the Unipolar Depression subgroup and the Barcelona Bipolar Disorders Programme. Both are part of the Spanish "Centro de Investigación Biomédica En Red en Salud Mental" (CIBERSAM), which is a Virtual Center of Network Research in Mental Health and Psychiatry, which has gathered the best research groups in Psychiatry and related disciplines in Spain. The Clinic-Affective Disorders research group has focused on the neurobiology (genetics, biomarkers, neuropsychology, neuroimaging), epidemiology (clinical subtypes, comorbidity, psychometric assessment, functionality), and treatment of bipolar and unipolar affective disorders (including pharmacological, biophysical, and psychosocial strategies). It has an outstanding and long tradition of collaborative research with national and international groups, and publishes over 60 original articles per year based on research findings, many of which have had significant impact on clinical practice

Individualizing treatment for patients with schizoaffective disorder

Compelling diagnostic definitions and evidence-based treatment recommendations for schizoaffective disorder are lacking, but clinicians can still develop an effective, individualized treatment regimen for patients with this condition. The steps necessary to help patients with schizoaffective disorder reach and maintain remission are to confirm the diagnosis, evaluate the patient's predictors of outcome, be aware of the available pharmacotherapeutic options and prescribe appropriate medications, and implement psychotherapy when patients achieve remission. In this brief activity, these essential steps are discussed and treatment recommendations are offered

Deconstructing Bipolar Disorder: A Critical Review of its Diagnostic Validity and a Proposal for DSM-V and ICD-11

The development of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and International Classification of Diseases, Eleventh Edition, deserves a significant conceptual step forward. There is a clear need to improve and refine the current diagnostic criteria, but also to introduce dimensions, perhaps not as an alternative but rather as a useful complement to categorical diagnosis. Laboratory, family, and treatment response data should also be systematically included in the diagnostic assessment when available. We have critically reviewed the content, concurrent, discriminant, and predictive validity of bipolar disorder, and to overcome the validity problems of the current classifications of mental disorders, we propose a modular system which may integrate categorical and dimensional issues, laboratory data, associated nonpsychiatric medical conditions, psychological assessment, and social issues in a comprehensive and nevertheless practical approach

Prediction of lithium response using genomic data

Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen's kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures

Phenomenology of manic episodes according to the presence or absence of depressive features as defined in DSM-5: Results from the IMPACT self-reported online survey

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Class effect of pharmacotherapy in bipolar disorder: fact or misbelief?

BACKGROUND: Anecdotal reports suggests that most clinicians treat medications as belonging to a class with regard to all therapeutic indications; this means that the whole 'class' of drugs is considered to possesses a specific therapeutic action. The present article explores the possible existence of a true 'class effect' for agents available for the treatment of bipolar disorder. METHODS: We reviewed the available treatment data from randomized controlled trials (RCTs) and explored 16 'agent class'/'treatment issue' cases for bipolar disorder. Four classes of agents were examined: first-generation antipsychotics (FGAs), second-generation antipsychotics (SGAs), antiepileptics and antidepressants, with respect to their efficacy on four treatment issues of bipolar disorder (BD) (acute mania, acute bipolar depression, maintenance against mania, maintenance against depression). RESULTS: From the 16 'agent class'/' treatment issue' cases, only 3 possible class effects were detected, and they all concerned acute mania and antipsychotics. Four effect cases have not been adequately studied (FGAs against acute bipolar depression and in maintenance protection from depression, and antidepressants against acute mania and protection from mania) and they all concern treatment cases with a high risk of switching to the opposite pole, thus research in these areas is poor. There is no 'class effect' at all concerning antiepileptics. CONCLUSIONS: The available data suggest that a 'class effect' is the exception rather than the rule in the treatment of BD. However, the possible presence of a 'class effect' concept discourages clinicians from continued scientific training and reading. Focused educational intervention might be necessary to change this attitude

Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning). Keywords: Brain; Cortical thickness; Gray matter; Mega-analysis; Neuroimaging; Schizophrenia; Volum