CLINICAL POTENTIAL OF CARIPRAZINE IN THE TREATMENT OF ACUTE MANIA

Cariprazine (RGH-188, trans-4-{2-[4-(2,3-dichlorophenyl)-piperazine-1-yl]-ethyl}-N,N-dimethylcarbamoyl-cyclohexyl-amine hydrochloride), is a novel antipsychotic with dopamine D2 and D3 receptors antagonist–partial agonist properties. Cariprazine has also moderate affinity for serotonin 5-hydroxytryptophan (5-HT) 1A receptors, high affinity for 5-HT1B receptors with pure antagonism and low affinity for 5-HT2A receptors. Randomized, double blind, placebo controlled, flexible-dose (3–12 mg/day) studies have demonstrated cariprazine is effective in both schizophrenia and acute manic episodes associated with bipolar disorder. The incidence of serious adverse events in cariprazine arm was no different than in placebo arm in these studies. The most common adverse events were extrapyramidal symptoms, headache, akathisia, constipation, nausea, and dyspepsia which can be explained with cariprazine’s partial dopamine agonism. Although cariprazine treatment was associated with a higher incidence of treatmentemergent adverse events, particularly akathisia and tremor, common side effects of marketed second generation antipsychotics such as weight gain, metabolic disturbances, prolactin increase or QTc prolongation were not associated with cariprazine, probably due to its moderate to low binding affinity for histamine H1 and 5-HT2C receptors. Animal studies show that cariprazine may have additional therapeutic benefit on impaired cognitive functioning with D3 receptor activity, however clinical data is still scarce. The aim of this article is to review the potential use of cariprazine for the treatment of acute manic episodes in the light of the preclinical and clinical trials reported to date

Biological Correlates of Empathy

Empathy can be defined as the capacity to know emotionally what another is experiencing from within the frame of reference of that other person and the capacity to sample the feelings of another or it can be metaphorized as to put oneself in another’s shoes. Although the concept of empathy was firstly described in psychological theories, researches studying the biological correlates of psychological theories have been increasing recently. Not suprisingly, dinamically oriented psychotherapists Freud, Kohut, Basch and Fenichel had suggested theories about the biological correlates of empathy concept and established the basis of this modality decades ago. Some other theorists emphasized the importance of empathy in the early years of lifetime regarding mother-child attachment in terms of developmental psychology and investigated its role in explanation of psychopathology. The data coming from some of the recent brain imaging and animal model studies also seem to support these theories. Although increased activity in different brain regions was shown in many of the brain imaging studies, the role of cingulate cortex for understanding mother-child relationship was constantly emphasized in nearly all of the studies. In addition to these studies, a group of Italian scientists has defined a group of neurons as “mirror neurons” in their studies observing rhesus macaque monkeys. Later, they also defined mirror neurons in human studies, and suggested them as “empathy neurons”. After the discovery of mirror neurons, the hopes of finding the missing part of the puzzle for understanding the biological correlates of empathy raised again. Although the roles of different biological parameters such as skin conductance and pupil diameter for defining empathy have not been certain yet, they are going to give us the opportunity to revise the inconsistent basis of structural validity in psychiatry and to stabilize descriptive validity. In this review, the possible neurobiological background of empathy will be discussed in the light of the recent brain imaging and animal studies

Genetic and Environmental Influences on the Affective Regulation Network: A Prospective Experience Sampling Analysis

Background: The study of networks of affective mental states that play a role in psychopathology may help model the influence of genetic and environmental risks. The aim of the present paper was to examine networks of affective mental states (AMS: “cheerful,” “insecure,” “relaxed,” “anxious,” “irritated,” and “down”) over time, stratified by genetic liability for psychopathology and exposure to environmental risk, using momentary assessment technology.Methods: Momentary AMS, collected using the experience sampling method (ESM) as well as childhood trauma and genetic liability (based on the level of shared genes and psychopathology in the co-twin) were collected in a population-based sample of female-female twin pairs and sisters (585 individuals). Networks were generated using multilevel time-lagged regression analysis, and regression coefficients were compared across three strata of childhood trauma severity and three strata of genetic liability using permutation testing. Regression coefficients were presented as network connections.Results: Visual inspection of network graphs revealed some suggestive changes in the networks with more exposure to either childhood trauma or genetic liability (i.e., stronger reinforcing loops between the three negative AMS anxious, insecure, and down both under higher early environmental, and under higher genetic liability exposure, stronger negative association between AMS of different valences: i.e., between “anxious” at t-1 and “relaxed” at t, “relaxed” at t-1 and “down” at t, under intermediate genetic liability exposure when compared to both networks under low and high genetic liability). Yet, statistical evaluation of differences across exposure strata was inconclusive.Conclusions: Although suggestive of a difference in the emotional dynamic, there was no conclusive evidence that genetic and environmental factors may impact ESM network models of individual AMS

Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches

The ubiquity of smartphones have opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitudinal data of participants' daily life experiences and is ideal to capture fluctuations in emotions (momentary mental states) as an indicator for later mental ill-health. In this study, ESM data of patients with psychosis spectrum disorder and controls were used to examine daily life emotions and higher order patterns thereof. We attempted to determine whether aggregated ESM data, in which statistical measures represent the distribution and dynamics of the original data, were able to distinguish patients from controls in a predictive modelling framework. Variable importance, recursive feature elimination, and ReliefF methods were used for feature selection. Model training, tuning, and testing were performed in nested cross-validation, based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Logistic Regression and Neural Networks. ROC analysis was used to post-process these models. Stability of model performance was studied using Monte Carlo simulations. The results provide evidence that patterns in emotion changes can be captured by applying a combination of these techniques. Acceleration in the variables anxious and insecure was particularly successful in adding further predictive power to the models. The best results were achieved by Support Vector Machines with radial kernel (accuracy=82% and sensitivity=82%). This proof-of-concept work demonstrates that synergistic machine learning and statistical modeling may be used to harness the power of ESM data in the future

Resilience Against Traumatic Stress: Current Developments and Future Directions

Given the high prevalence of stress-related mental disorders, their impact on person, family, and society and the paucity of treatment options for most of these disorders, there is currently a pressing need for innovative approaches to deal with these issues and enhance well-being. One approach which has received increasing attention over the last decade is to shift our scientific and clinical focus from risk factors for psychopathology to factors promoting resilience and mental well-being. In order to summarize and evaluate the current state of scientific affairs on the biological basis of resilience, we provide an overview of the literature on animal and human studies of resilience. Because resilience can only truly be operationalized through longitudinal data collection and analyses, we focus primarily on longitudinal studies. This review shows that the concept of resilience is currently being operationalized, measured and even defined in widely variable manners, both within animal and human studies. We further provide an overview of existing and new strategies that could help promote resilience and which are proposed to be implemented more often in clinical situations. Finally, we summarize the challenges the field is facing and provide recommendations for future research

Predicting Psychosis Using the Experience Sampling Method with Mobile Apps

Smart phones have become ubiquitous in the recent years, which opened up a new opportunity for rediscovering the Experience Sampling Method (ESM) in a new efficient form using mobile apps, and provides great prospects to become a low cost and high impact mHealth tool for psychiatry practice. The method is used to collect longitudinal data of participants' daily life experiences, and is ideal to capture fluctuations in emotions (momentary mental states) as an early indicator for later mental health disorder. In this study ESM data of patients with psychosis and controls were used to examine emotion changes and identify patterns. This paper attempts to determine whether aggregated ESM data, in which statistical measures represent the distribution and dynamics of the original data, are able to distinguish patients from controls. Variable importance, recursive feature elimination and ReliefF methods were used for feature selection. Model training and tuning, and testing were performed in nested cross-validation, and were based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Logistic Regression and Neural Networks. ROC analysis was used to post-process these models. Stability of model performances was studied using Monte Carlo simulations. The results provide evidence that pattern in mood changes can be captured with the combination of techniques used. The best results were achieved by SVM with radial kernel, where the best model performed with 82% accuracy and 82% sensitivity

Associations between genetic liabilities to smoking behavior and schizophrenia symptoms in patients with a psychotic disorder, their siblings and healthy controls

It is unknown how smoking behavior polygenic scores (PRS) relate to psychosis and psychotic symptoms. To elucidate this, genotype and phenotype data were collected from patients with schizophrenia, their unaffected siblings, and healthy controls in a six-year follow-up prospective cohort study. Associations between smoking behaviors, PRS and schizophrenia symptoms were explored using linear mixed-effect models. The mean number of cigarettes smoked per day were 18 for patients, 13 for siblings and 12 for controls. In the overall sample, PRSs-smoking initiation (i.e., ever smoking as a binary phenotype, PRS-SI) were positively associated with positive symptoms, negative symptoms, and depressive symptoms, whereas PRSs-AI (age at regular smoking initiation) were negatively associated with all symptom dimensions, with similar effect sizes. When considering groups separately, PRS were only associated with psychotic symptoms in siblings and controls. In conclusion, unaffected siblings show smoking behaviors at an intermediate level between patients and healthy controls. Additionally, PRS-SI and PRS-AI are associated with all symptom dimensions only in unaffected siblings and healthy controls, possibly owing to the dominant role of other (genetic) risk factors in patients. Future studies may examine mechanisms via which genetic risk for smoking affects mental health symptoms.</p

Be(com)ing Social:Daily-Life Social Interactions and Parental Bonding

Parents are known to provide a lasting basis for their children's social development. Understanding parent-driven socialization is particularly relevant in adolescence, as an increasing social independence is developed. However, the relationship between key parenting styles of care and control and the microlevel expression of daily-life social interactions has been insufficiently studied. Adolescent and young adult twins and their nontwin siblings (N = 635; mean age = 16.6; age range = 14.2-21.9; 58.6% female; 79.5% in or having completed higher secondary/tertiary education; 2.8% speaking language other than Dutch at home) completed the Parental Bonding Instrument (PBI) on parental care and control. Participants also completed a 6-day experience sampling period (10 daily beeps, mean compliance = 68.0%) to assess daily-life social interactions. Higher overall parental bonding quality (of both parents) related to more positive social experiences in daily life (e.g., belonging in company), but not to more social behaviors (e.g., being with others). Factor analysis indicated a three-factor structure of the PBI, with care, denial of psychological autonomy, and encouragement of behavioral freedom. Paternal care was uniquely predictive of better social experiences. These findings demonstrate how parenting styles may be uniquely associated with how adolescents experience their social world, with a potentially important role for fathers in particular. This complements the long-held idea of socialization through parenting by bringing it into the context of daily life and implies how both conceptualizations of social functioning and interventions aimed at alleviating social dysfunction might benefit from a stronger consideration of day-to-day social experiences.</p

Emotion regulation in response to daily negative and positive events in youth:The role of event intensity and psychopathology

Environmental and individual contextual factors profoundly influence how people regulate their emotions. The current article addresses the role of event intensity and psychopathology (an admixture of depression, anxiety, and psychoticism) on emotion regulation in response to naturally occurring events. For six days each evening, a youth sample (aged 15-25, N = 713) recorded the intensity of the most positive and most negative event of the day and their subsequent emotion regulation. The intensity of negative events was positively associated with summed total emotion regulation effort, strategy diversity, engaging in rumination, situation modification, emotion expression, and sharing and negatively associated with reappraisal and acceptance. The intensity of positive events was positively associated with strategy diversity, savoring, emotion expression, and sharing. Higher psychopathology symptoms were only related to ruminating more about negative events. We interpret these findings as support for the role of context in the degree of effort and type of emotion regulation that young people engage in

Phenome-wide and genome-wide analyses of quality of life in schizophrenia

This article has been published in a revised form in BJPsych Open [http://doi.org/10.1192/bjo.2020.140]. This version is free to view and download for private research and study only. Not for re-distribution or re-use. © copyright holder.Background Schizophrenia negatively affects quality of life (QoL). A handful of variables from small studies have been reported to influence QoL in patients with schizophrenia, but a study comprehensively dissecting the genetic and non-genetic contributing factors to QoL in these patients is currently lacking. Aims We adopted a hypothesis-generating approach to assess the phenotypic and genotypic determinants of QoL in schizophrenia. Method The study population comprised 1119 patients with a psychotic disorder, 1979 relatives and 586 healthy controls. Using linear regression, we tested >100 independent demographic, cognitive and clinical phenotypes for their association with QoL in patients. We then performed genome-wide association analyses of QoL and examined the association between polygenic risk scores for schizophrenia, major depressive disorder and subjective well-being and QoL. Results We found nine phenotypes to be significantly and independently associated with QoL in patients, the most significant ones being negative (β = −1.17; s.e. 0.05; P = 1 × 10–83; r2 = 38%), depressive (β = −1.07; s.e. 0.05; P = 2 × 10–79; r2 = 36%) and emotional distress (β = −0.09; s.e. 0.01; P = 4 × 10–59, r2 = 25%) symptoms. Schizophrenia and subjective well-being polygenic risk scores, using various P-value thresholds, were significantly and consistently associated with QoL (lowest association P-value = 6.8 × 10–6). Several sensitivity analyses confirmed the results. Conclusions Various clinical phenotypes of schizophrenia, as well as schizophrenia and subjective well-being polygenic risk scores, are associated with QoL in patients with schizophrenia and their relatives. These may be targeted by clinicians to more easily identify vulnerable patients with schizophrenia for further social and clinical interventions to improve their QoL.Dutch Health Research Council; Lundbeck; AstraZeneca; Eli Lilly; Janssen Cilag;Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions at Geestelijke Gezondheidszorg (GGZ) Ingeest; Arkin; Dijk en Duin; GGZ Rivierduinen; Erasmus Medical Centre and GGZ Noord Holland Noord; Groningen: University Medical Center Groningen and the mental health institutions at Lentis; GGZ Friesland; GGZ Drenthe; Dimence; Mediant; GGNet Warnsveld; Yulius Dordrecht and Parnassia Psycho-Medical Center The Hague; Maastricht: Maastricht University Medical Centre and the mental health institutions at GGZ Eindhoven en De Kempen; GGZ Breburg; GGZ Oost-Brabant; Vincent van Gogh voor Geestelijke Gezondheid; Mondriaan; Virenze riagg; Zuyderland GGZ; MET ggz; Universitair Centrum Sint-Jozef Kortenberg; Collaborative Antwerp Psychiatric Research Institute University of Antwerp; Psychiatrisch Centrum Ziekeren Sint-Truiden; Psychiatrisch Ziekenhuis Sancta Maria Sint-Truiden; GGZ Overpelt and Openbaar Psychiatrisch Zorgcentrum Rekem; Utrecht: University Medical Center Utrecht and the mental health institutions Altrecht; GGZ Centraal and Delt