Psychomotor Retardation and the prognosis of antidepressant treatment in patients with unipolar Psychotic Depression

Background: Psychomotor Retardation is a key symptom of Major Depressive Disorder. According to the literature its presence may affect the prognosis of treatment. Aim of the present study is to investigate the prognostic role of Psychomotor Retardation in patients with unipolar Psychotic Depression who are under antidepressant treatment. Methods: The Salpetriere Retardation Rating Scale was administered at baseline and after 6 weeks to 122 patients with unipolar Psychotic Depression who were randomly allocated to treatment with imipramine, venlafaxine or venlafaxine plus quetiapine. We studied the effects of Psychomotor Retardation on both depression and psychosis related outcome measures. Results: 73% of the patients had Psychomotor Retardation at baseline against 35% after six weeks of treatment. The presence of Psychomotor Retardation predicted lower depression remission rates in addition to a higher persistence of delusions. After six weeks of treatment, venlafaxine was associated with higher levels of Psychomotor Retardation compared to imipramine and venlafaxine plus quetiapine. Conclusions: Our data confirm that Psychomotor Retardation is a severity marker of unipolar Psychotic Depression. It is highly prevalent and predicts lower effectivity of antidepressant psychopharmacological treatment

Harmonization of Neuroticism and Extraversion phenotypes across inventories and cohorts in the Genetics of Personality Consortium : an application of Item Response Theory

Peer reviewe

Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively small sample sizes of those studies. Here, we report on a large meta-analysis of GWA studies for extraversion in 63,030 subjects in 29 cohorts. Extraversion item data from multiple personality inventories were harmonized across inventories and cohorts. No genome-wide significant associations were found at the single nucleotide polymorphism (SNP) level but there was one significant hit at the gene level for a long non-coding RNA site (LOC101928162). Genome-wide complex trait analysis in two large cohorts showed that the additive variance explained by common SNPs was not significantly different from zero, but polygenic risk scores, weighted using linkage information, significantly predicted extraversion scores in an independent cohort. These results show that extraversion is a highly polygenic personality trait, with an architecture possibly different from other complex human traits, including other personality traits. Future studies are required to further determine which genetic variants, by what modes of gene action, constitute the heritable nature of extraversion

Meta-analysis of genome-wide association studies for extraversion:Findings from the Genetics of Personality Consortium

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively small sample sizes of those studies. Here, we report on a large meta-analysis of GWA studies for extraversion in 63,030 subjects in 29 cohorts. Extraversion item data from multiple personality inventories were harmonized across inventories and cohorts. No genome-wide significant associations were found at the single nucleotide polymorphism (SNP) level but there was one significant hit at the gene level for a long non-coding RNA site (LOC101928162). Genome-wide complex trait analysis in two large cohorts showed that the additive variance explained by common SNPs was not significantly different from zero, but polygenic risk scores, weighted using linkage information, significantly predicted extraversion scores in an independent cohort. These results show that extraversion is a highly polygenic personality trait, with an architecture possibly different from other complex human traits, including other personality traits. Future studies are required to further determine which genetic variants, by what modes of gene action, constitute the heritable nature of extraversion

Understanding disease processes by partitioned dynamic Bayesian networks

For many clinical problems in patients the underlying pathophysiological process changes in the course of time as a result of medical interventions. In model building for such problems, the typical scarcity of data in a clinical setting has been often compensated by utilizing time homogeneous models, such as dynamic Bayesian networks. As a consequence, the specificities of the underlying process are lost in the obtained models. In the current work, we propose the new concept of partitioned dynamic Bayesian networks to capture distribution regime changes, i.e. time non-homogeneity, benefiting from an intuitive and compact representation with the solid theoretical foundation of Bayesian network models. In order to balance specificity and simplicity in real-world scenarios, we propose a heuristic algorithm to search and learn these non-homogeneous models taking into account a preference for less complex models. An extensive set of experiments were ran, in which simulating experiments show that the heuristic algorithm was capable of constructing well-suited solutions, in terms of goodness of fit and statistical distance to the original distributions, in consonance with the underlying processes that generated data, whether it was homogeneous or non-homogeneous. Finally, a study case on psychotic depression was conducted using non-homogeneous models learned by the heuristic, leading to insightful answers for clinically relevant questions concerning the dynamics of this mental disorder. (C) 2016 Elsevier Inc. All rights reserved

BDNF in late-life depression: Effect of SSRI usage and interaction with childhood abuse

Brain-Derived Neurotrophic Factor (BDNF) serum levels are abnormally low in depressed patients as compared to healthy controls and normalize with SSRI treatment. The aim of this study is to examine serum BDNF levels in late-life depression, stratified for SSRI usage, and to explore the relation between BDNF levels and specific depression characteristics as well as between BDNF levels and early and recent life stressors in late-life depression. We assessed serum BDNF levels in 259 depressed patients not using an SSRI, 99 depressed patients using an SSRI and 119 non-depressed controls (age range 60-93 years). Depressive disorders were diagnosed with the Composite International Diagnostic Interview (CIDI, version 2.1). Serum BDNF levels were significantly higher in depressed patients who used an SSRI compared to depressed patients not using SSRIs and compared to non-depressed controls, when adjusted for age, sex, life style characteristics, cognitive functioning and somatic comorbidity. Recent life-events, assessed with the List of Threatening Events-Questionnaire, were significantly associated with lower BDNF levels in non-depressed subjects only. Although a summary score of early traumatization (before the age of 16 years) was not associated with serum BDNF levels in any of the three groups, we found an interaction between a history of severe physical abuse and SSRI usage in the depressed group. Interestingly, higher serum levels of BDNF in depressed patients using SSRIs were only found in those patients without a history of severe childhood abuse and not in those with a history of severe childhood abuse

Multi-omics data integration methods and their applications in psychiatric disorders

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.</p