Concerted transcriptional regulation of the morphogenesis of hypothalamic neurons by ONECUT3.

Acquisition of specialized cellular features is controlled by the ordered expression of transcription factors (TFs) along differentiation trajectories. Here, we find a member of the Onecut TF family, ONECUT3, expressed in postmitotic neurons that leave their Ascl1+/Onecut1/2+ proliferative domain in the vertebrate hypothalamus to instruct neuronal differentiation. We combined single-cell RNA-seq and gain-of-function experiments for gene network reconstruction to show that ONECUT3 affects the polarization and morphogenesis of both hypothalamic GABA-derived dopamine and thyrotropin-releasing hormone (TRH)+ glutamate neurons through neuron navigator-2 (NAV2). In vivo, siRNA-mediated knockdown of ONECUT3 in neonatal mice reduced NAV2 mRNA, as well as neurite complexity in Onecut3-containing neurons, while genetic deletion of Onecut3/ceh-48 in C. elegans impaired neurocircuit wiring, and sensory discrimination-based behaviors. Thus, ONECUT3, conserved across neuronal subtypes and many species, underpins the polarization and morphological plasticity of phenotypically distinct neurons that descend from a common pool of Ascl1+ progenitors in the hypothalamus.

Machine learning models predict PTSD severity and functional impairment: A personalized medicine approach for uncovering complex associations among heterogeneous symptom profiles.

OBJECTIVE: Posttraumatic stress disorder (PTSD) is a debilitating psychiatric illness, experienced by approximately 10% of the population. Heterogeneous presentations that include heightened dissociation, comorbid anxiety and depression, and emotion dysregulation contribute to the severity of PTSD, in turn, creating barriers to recovery. There is an urgent need to use data-driven approaches to better characterize complex psychiatric presentations with the aim of improving treatment outcomes. We sought to determine if machine learning models could predict PTSD-related illness in a real-world treatment-seeking population using self-report clinical data. METHOD: Secondary clinical data from 2017 to 2019 included pretreatment measures such as trauma-related symptoms, other mental health symptoms, functional impairment, and demographic information from adults admitted to an inpatient unit for PTSD in Canada (n = 393). We trained two nonlinear machine learning models (extremely randomized trees) to identify predictors of (a) PTSD symptom severity and (b) functional impairment. We assessed model performance based on predictions in novel subsets of patients. RESULTS: Approximately 43% of the variance in PTSD symptom severity (R²avg = .43, R²median = .44, p = .001) was predicted by symptoms of anxiety, dissociation, depression, negative trauma-related beliefs about others, and emotion dysregulation. In addition, 32% of the variance in functional impairment scores (R²avg = .32, R²median = .33, p = .001) was predicted by anxiety, PTSD symptom severity, cognitive dysfunction, dissociation, and depressive symptoms. CONCLUSIONS: Our results reinforce that dissociation, cooccurring anxiety and depressive symptoms, maladaptive trauma appraisals, cognitive dysfunction, and emotion dysregulation are critical targets for trauma-related interventions. Machine learning models can inform personalized medicine approaches to maximize trauma recovery in real-world inpatient populations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Predictors and motives for mask-wearing behavior and vaccination intention.

Containing a pandemic requires that individuals adhere to measures such as wearing face-masks and getting vaccinated. Therefore, identifying predictors and motives for both behaviors is of importance. Here, we study the decisions made by a cross-national sample in randomized hypothetical scenarios during the COVID-19 pandemic. Our results show that mask-wearing was predicted by empathic tendencies, germ aversion, and higher age, whilst belief in misinformation and presentation of an interaction partner as a family member lowered the safety standards. The main motives associated with taking the mask off included: rationalization, facilitating interaction, and comfort. Vaccination intention was positively predicted by empathy, and negatively predicted by belief in misinformation and higher costs of the vaccine. We found no effect of immunization status of the surrounding social group. The most common motive for vaccination was protection of oneself and others, whereas undecided and anti-vaccine groups reported doubts about the effectiveness and fear of side effects. Together, we identify social and psychological predictors and motives of mask-wearing behavior and vaccination intention. The results highlight the importance of social context for mask-wearing, easy access to vaccines, empathy, and trust in publicly distributed information.

Securing Your Relationship: Quality of Intimate Relationships During the COVID-19 Pandemic Can Be Predicted by Attachment Style.

The COVID-19 pandemic along with the restrictions that were introduced within Europe starting in spring 2020 allows for the identification of predictors for relationship quality during unstable and stressful times. The present study began as strict measures were enforced in response to the rising spread of the COVID-19 virus within Austria, Poland, Spain and Czech Republic. Here, we investigated quality of romantic relationships among 313 participants as movement restrictions were implemented and subsequently phased out cross-nationally. Participants completed self-report questionnaires over a period of 7 weeks, where we predicted relationship quality and change in relationship quality using machine learning models that included a variety of potential predictors related to psychological, demographic and environmental variables. On average, our machine learning models predicted 29% (linear models) and 22% (non-linear models) of the variance with regard to relationship quality. Here, the most important predictors consisted of attachment style (anxious attachment being more influential than avoidant), age, and number of conflicts within the relationship. Interestingly, environmental factors such as the local severity of the pandemic did not exert a measurable influence with respect to predicting relationship quality. As opposed to overall relationship quality, the change in relationship quality during lockdown restrictions could not be predicted accurately by our machine learning models when utilizing our selected features. In conclusion, we demonstrate cross-culturally that attachment security is a major predictor of relationship quality during COVID-19 lockdown restrictions, whereas fear, pathogenic threat, sexual behavior, and the severity of governmental regulations did not significantly influence the accuracy of prediction.

Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study.

During medical pandemics, protective behaviors need to be motivated by effective communication, where finding predictors of fear and perceived health is of critical importance. The varying trajectories of the COVID-19 pandemic in different countries afford the opportunity to assess the unique influence of 'macro-level' environmental factors and 'micro-level' psychological variables on both fear and perceived health. Here, we investigate predictors of fear and perceived health using machine learning as lockdown restrictions in response to the COVID-19 pandemic were introduced in Austria, Spain, Poland and Czech Republic. Over a seven-week period, 533 participants completed weekly self-report surveys which measured the target variables subjective fear of the virus and perceived health, in addition to potential predictive variables related to psychological factors, social factors, perceived vulnerability to disease (PVD), and economic circumstances. Viral spread, mortality and governmental responses were further included in the analysis as potential environmental predictors. Results revealed that our models could accurately predict fear of the virus (accounting for approximately 23% of the variance) using predictive factors such as worrying about shortages in food supplies and perceived vulnerability to disease (PVD), where interestingly, environmental factors such as spread of the virus and governmental restrictions did not contribute to this prediction. Furthermore, our results revealed that perceived health could be predicted using PVD, physical exercise, attachment anxiety and age as input features, albeit with smaller effect sizes. Taken together, our results emphasize the importance of 'micro-level' psychological factors, as opposed to 'macro-level' environmental factors, when predicting fear and perceived health, and offer a starting point for more extensive research on the influences of pathogen threat and governmental restrictions on the psychology of fear and health.

Development of Rasch-based short screenings for the assessment of treatment motivation in patients with cardiovascular diseases.

Purpose: The aim of the study was to develop unidimensional test-fair and economic short screenings to assess treatment motivation in patients with cardiovascular diseases using the Rasch analysis.Materials and methods: After pretesting for relevance and comprehension, a pool of 132 items on treatment motivation was completed by a sample consisting of 1168 patients with cardiovascular diseases recruited in two German cardiological rehabilitation centers. Confirmatory factor analyses and the Rasch analyses were conducted.Results: The confirmatory factor analyses confirmed a three-factor structure of the treatment motivation construct with task self-efficacy, outcome expectancies and intention as factors. Using the Rasch analysis for each of the three factors and removing items with misfit, differential item functioning and local response dependency reduced the initial item pool to the three short screenings. The short screenings fit to the Rasch model with a root mean square error of approximation (RMSEA = 0.021 (task self-efficacy; seven items); RMSEA = 0.024 (outcome expectancies; 12 items), RMSEA = 0.027 (intention; nine items). Person-separation reliability was 0.81, 0.82, and 0.73. Unidimensionality could be verified.Conclusions: The calibrated, unidimensional short screenings provide a psychometrically sound option for an initial- and follow-up assessment of treatment motivation in rehabilitation patients with cardiovascular diseases. Further testing in other cardiovascular diseases populations is needed to increase generalizability.Implications for rehabilitationNew short screenings for the assessment of treatment motivation: task self-efficacy, outcome expectancies, intention in rehabilitation patients with cardiovascular diseases are available.Treatment motivation short screeningsself-efficacy/outcome expectancies/intention consist of seven items (treatment motivation short screeningself-efficacy), 12 items (treatment motivation short screeningoutcome expectancies), nine items (treatment motivation short screeningintention) and are therefore especially timesaving.The short screenings demonstrate good psychometric properties, cover a wide spectrum of task self-efficacy, outcome expectancies and intention, and are free of local dependencies and of differential item functioning regarding to gender, age and cardiovascular diagnoses.Using a Rasch based unidimensional short screening is a test-fair and economic method to assess patients' treatment motivation, which might help to improve rehabilitation health care tailored to patients' needs.