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Background and Hypothesis: When occurring in adolescence, psychotic experiences (PE), subclinical psychotic symptoms, can be an early marker of mental illnesses. Studies with high-risk populations for psychosis show that anxiety symptoms often precede the onset of psychosis. Although anxiety symptoms are frequently experienced across the continuum of psychosis, no previous study has analyzed this association using a cross-lagged panel model (CLPM) longitudinally to identify if anxiety can be a predictor of PE over time or vice versa. The aim of the current study was to investigate whether one symptom domain predicts the other over time. Study Design: 2194 children from the Brazilian High-Risk Cohort (BHRC) were evaluated at baseline (T 0), and 76.5% completed a 3-year follow-up (T 1) interview. Childhood anxiety symptoms and PE were assessed using a standardized self-report questionnaire at both time points. Cross-lagged panel models evaluated time-lagged associations between PE and anxiety longitudinally. Study Results: Higher levels of anxiety in childhood predicted an increase in PE levels in adolescence. The cross-lagged effect of anxiety scores at T 0 on PE scores at T 1 was significant (ß = .03, SE = 0.01, P ≤ .001) and PE in childhood did not increase levels of anxiety in adolescence, when controlling for sociodemographic and clinical characteristics. Conclusions: Our findings reinforce that anxiety may represent an early marker of psychosis proneness, not a consequence of already presenting PE, which can help to develop better screening approaches. Therefore, future studies should focus on identifying biological or other clinical markers to increase prediction accuracy.

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Importance: The period from childhood to early adulthood involves increased susceptibility to the onset of mental disorders, with implications for policy making that may be better appreciated by disaggregated analyses of narrow age groups. Objective: To estimate the global prevalence and years lived with disability (YLDs) associated with mental disorders and substance use disorders (SUDs) across 4 age groups using data from the 2019 Global Burden of Disease (GBD) study. Design, Setting, and Participants: Data from the 2019 GBD study were used for analysis of mental disorders and SUDs. Results were stratified by age group (age 5 to 9, 10 to 14, 15 to 19, and 20 to 24 years) and sex. Data for the 2019 GBD study were collected up to 2018, and data were analyzed for this article from April 2022 to September 2023. Exposure: Age 5 to 9 years, 10 to 14 years, 15 to 19 years, and 20 to 24 years. Main Outcomes and Measures: Prevalence rates with 95% uncertainty intervals (95% UIs) and number of YLDs. Results: Globally in 2019, 293 million of 2516 million individuals aged 5 to 24 years had at least 1 mental disorder, and 31 million had an SUD. The mean prevalence was 11.63% for mental disorders and 1.22% for SUDs. For the narrower age groups, the prevalence of mental disorders was 6.80% (95% UI, 5.58-8.03) for those aged 5 to 9 years, 12.40% (95% UI, 10.62-14.59) for those aged 10 to 14 years, 13.96% (95% UI, 12.36-15.78) for those aged 15 to 19 years, and 13.63% (95% UI, 11.90-15.53) for those aged 20 to 24 years. The prevalence of each individual disorder also varied by age groups; sex-specific patterns varied to some extent by age. Mental disorders accounted for 31.14 million of 153.59 million YLDs (20.27% of YLDs from all causes). SUDs accounted for 4.30 million YLDs (2.80% of YLDs from all causes). Over the entire life course, 24.85% of all YLDs attributable to mental disorders were recorded before age 25 years. Conclusions and Relevance: An analytical framework that relies on stratified age groups should be adopted for examination of mental disorders and SUDs from childhood to early adulthood. Given the implications of the early onset and lifetime burden of mental disorders and SUDs, age-disaggregated data are essential for the understanding of vulnerability and effective prevention and intervention initiatives.

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OBJECTIVE: The Identifying Depression Early in Adolescence Risk Score (IDEA-RS) was recently developed in Brazil using data from the Pelotas 1993 Birth Cohort to estimate the individualized probability of developing depression in adolescence. This model includes 11 sociodemographic variables and has been assessed in longitudinal studies from four other countries. We aimed to test the performance of IDEA-RS in an independent, community-based, school-attending sample within the same country: the Brazilian High-Risk Cohort. METHODS: Standard external validation, refitted, and case mix-corrected models were used to predict depression among 1442 youth followed from a mean age of 13.5 years at baseline to 17.7 years at follow-up, using probabilities calculated with IDEA-RS coefficients. RESULTS: The area under the curve was 0.65 for standard external validation, 0.70 for the case mix-corrected model, and 0.69 for the refitted model, with discrimination consistently above chance for predicting depression in the new dataset. There was some degree of miscalibration, corrected by model refitting (calibration-in-the-large reduced from 0.77 to 0). CONCLUSION: IDEA-RS was able to parse individuals with higher or lower probability of developing depression beyond chance in an independent Brazilian sample. Further steps should include model improvements and additional studies in populations with high levels of subclinical symptoms to improve clinical decision making.

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BACKGROUND: Specific pathways of intergenerational transmission of behavioral traits remain unclear. Here, we aim to investigate how parental genetics influence offspring cognition, educational attainment, and psychopathology in youth. METHODS: Participants for the discovery sample were 2,189 offspring (aged 6-14 years), 1898 mothers and 1,017 fathers who underwent genotyping, psychiatric, and cognitive assessments. We calculated polygenic scores (PGS) for cognition, educational attainment, attention-deficit hyperactivity disorder (ADHD), and schizophrenia for the trios. Phenotypes studied included educational and cognitive measures, ADHD and psychotic symptoms. We used a stepwise approach and multiple mediation models to analyze the effect of parental PGS on offspring traits via offspring PGS and parental phenotype. Significant results were replicated in a sample of 1,029 adolescents, 363 mothers, and 307 fathers. RESULTS: Maternal and paternal PGS for cognition influenced offspring general intelligence and executive function via offspring PGS (genetic pathway) and parental education (phenotypic pathway). Similar results were found for parental PGS for educational attainment and offspring reading and writing skills. These pathways fully explained associations between parental PGS and offspring phenotypes, without residual direct association. Associations with maternal, but not paternal, PGS were replicated. No associations were found between parental PGS for psychopathology and offspring specific symptoms. CONCLUSIONS: Our findings indicate that parental genetics influences offspring cognition and educational attainment by genetic and phenotypic pathways, suggesting the expression of parental phenotypes partially explain the association between parental genetic risk and offspring outcomes. Multiple mediations might represent an effective approach to disentangle distinct pathways for intergenerational transmission of behavioral traits.

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BACKGROUND: Very few predictive models in Psychiatry had their performance validated in independent external samples. A previously developed multivariable demographic model for attention-deficit/hyperactivity disorder (ADHD) accurately predicted young adulthood ADHD using clinical and demographical information collected in childhood in three samples from developed countries, but failed to replicate its performance in a sample from a developing country. Furthermore, consolidated risk factors for ADHD were not included among its predictors. METHODS: Participants were 1905 children and adolescents from a community-based sample and followed from ages 6 to 14 years at baseline to ages 14 to 23 years (mean age 18) at follow-up. We applied the intercept and weights of the original model to the data, calculating the predicted probability of each participant according to the set of predictors collected in childhood, and compared the estimates with the actual outcome (ADHD) collected during adolescence and young adulthood. We explored the performance of the original model, and of models including novel predictors (prematurity, family history of ADHD, and polygenic risk score for ADHD). RESULTS: The observed area under the curve of the original model was .76 (95% Confidence Interval .70 to .82). The multivariable demographical model outperformed single variable models using only prematurity, family history, or the ADHD PRS. Adding either of these variables, or all at once, did not improve the performance of the original demographical model. CONCLUSIONS: Our findings suggest that the originally developed ADHD predictive model is suitable for use in different settings for clinical and research purposes.

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BACKGROUND: Understanding deviations from typical brain development is a promising approach to comprehend pathophysiology in childhood and adolescence. We investigated if cerebellar volumes different than expected for age and sex could predict psychopathology, executive functions and academic achievement. METHODS: Children and adolescents aged 6-17 years from the Brazilian High-Risk Cohort Study for Mental Conditions had their cerebellar volume estimated using Multiple Automatically Generated Templates from T1-weighted images at baseline (n = 677) and at 3-year follow-up (n = 447). Outcomes were assessed using the Child Behavior Checklist and standardized measures of executive functions and school achievement. Models of typically developing cerebellum were based on a subsample not exposed to risk factors and without mental-health conditions (n = 216). Deviations from this model were constructed for the remaining individuals (n = 461) and standardized variation from age and sex trajectory model was used to predict outcomes in cross-sectional, longitudinal and mediation analyses. RESULTS: Cerebellar volumes higher than expected for age and sex were associated with lower externalizing specific factor and higher executive functions. In a longitudinal analysis, deviations from typical development at baseline predicted inhibitory control at follow-up, and cerebellar deviation changes from baseline to follow-up predicted changes in reading and writing abilities. The association between deviations in cerebellar volume and academic achievement was mediated by inhibitory control. CONCLUSIONS: Deviations in the cerebellar typical development are associated with outcomes in youth that have long-lasting consequences. This study highlights both the potential of typical developing models and the important role of the cerebellum in mental health, cognition and education.

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Objective: The Identifying Depression Early in Adolescence Risk Score (IDEA-RS) was recently developed in Brazil using data from the Pelotas 1993 Birth Cohort to estimate the individualized probability of developing depression in adolescence. This model includes 11 sociodemographic variables and has been assessed in longitudinal studies from four other countries. We aimed to test the performance of IDEA-RS in an independent, community-based, school-attending sample within the same country: the Brazilian High-Risk Cohort. Methods: Standard external validation, refitted, and case mix-corrected models were used to predict depression among 1442 youth followed from a mean age of 13.5 years at baseline to 17.7 years at follow-up, using probabilities calculated with IDEA-RS coefficients. Results: The area under the curve was 0.65 for standard external validation, 0.70 for the case mix-corrected model, and 0.69 for the refitted model, with discrimination consistently above chance for predicting depression in the new dataset. There was some degree of miscalibration, corrected by model refitting (calibration-in-the-large reduced from 0.77 to 0). Conclusion: IDEA-RS was able to parse individuals with higher or lower probability of developing depression beyond chance in an independent Brazilian sample. Further steps should include model improvements and additional studies in populations with high levels of subclinical symptoms to improve clinical decision making.

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BACKGROUND: Young people can receive mental health care from many sources, from formal and informal sectors. Caregiver characteristics/experiences/beliefs may influence whether young people get help and the type of care or support used by their child. We investigate facilitators/barriers to receiving formal and/or informal care, particularly those related to the caregiver's profile. METHODS: We interviewed 1,400 Brazilian primary caregivers of young people (aged 10-19), participants of a high-risk cohort. Caregivers reported on young people's formal/informal mental health care utilization, and associated barriers and facilitators to care. Data were also collected on youth mental health and its impact on everyday life; and caregiver characteristics-education, socioeconomics, ethnicity, mental health, and stigma. Logistic regression models were used to examine the relationship between caregiver and young people characteristics with formal/informal care utilization. RESULTS: Persistence and greater impact of youth mental health conditions were associated with a higher likelihood of care, more clearly for formal care. Caregiver characteristics, however, also played a key role in whether young people received any care: lower parental stigma was associated with greater formal service use, and lower socioeconomic class showed higher odds of informal care (mainly from religious leaders). CONCLUSIONS: This study highlights the key role of the caregivers as gatekeepers to child treatment access, particularly parental stigma influencing whether young people received any mental health care, even in a low resource setting. These results help to map barriers for treatment access and delivery for young people, aiming to improve intervention efforts and mental health support.

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BACKGROUND: Lesbian, Gay, Bisexual, Transgender, Queer, and Asexual (LGBTQA+) youth have a greater chance of experiencing stressful life events when compared to cisgender heterosexual peers, which can lead to mental health problems. We aimed to estimate the prevalence of mental disorders among LGBTQA+ youths from two large cities in Brazil. METHODS: Participants were 13-22 years old youths from the 3rd wave of the Brazilian High-Risk Cohort for Psychiatric Disorders (n = 1475). Mental disorders were assessed using the Development and Well-Being Behavior Assessment. Sexual orientation and gender identity were assessed using a self-report confidential questionnaire. Data were analyzed through logistic regressions (adjusting for sociodemographic) using sampling weights to account for attrition and our oversampling high-risk design. RESULTS: 15.18% of the sample described themselves as LGBTQA+. The LGBTQA+ group presented higher rates of anxiety disorders (30.14% vs. 13.37%; OR = 3.37; 95%CI:2.51-4.50), depressive disorders (27.75% vs. 15.34%; OR = 2.17; 95%CI:1.60-2.93) and post-traumatic stress disorder (4.98% vs. 2.25%; OR = 4.20; 95%CI:2.24-7.82), if compared with the cisgender heterosexual group. No difference was found for conduct disorders (2.97% vs. 5.21%; OR = 0.82; 95%CI:0.35-1.65) or attention deficit hyperactivity disorder (5.92% vs. 3.28%; OR = 1.56; 95%CI:0.83-2.79). LIMITATIONS: Although recruitment was performed at 57 schools in the two cities, sampling was non-probabilistic and included only urban areas, which might bias prevalence estimates and group comparisons. CONCLUSIONS: Our results elucidate the mental health disparities between LGBTQA+ people and cisgender heterosexuals in Brazil. It highlights the need to promote the inclusion of this population in policy formulation and support actions to mitigate the suffering related to sexual orientation and gender identity.

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While classically linked to memory, the hippocampus is also a feeding behavior modulator due to its multiple interconnected pathways with other brain regions and expression of receptors for metabolic hormones. Here we tested whether variations in insulin sensitivity would be correlated with differential brain activation following exposure to palatable food cues, as well as with variations in implicit food memory in a cohort of healthy adolescents, some of whom were born small for gestational age (SGA). Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) was positively correlated with activation in the cuneus, and negatively correlated with activation in the middle frontal lobe, superior frontal gyrus and precuneus when presented with palatable food images versus non-food images in healthy adolescents. Additionally, HOMA-IR and insulinemia were higher in participants with impaired food memory. SGA individuals had higher snack caloric density and greater chance for impaired food memory. There was also an interaction between the HOMA-IR and birth weight ratio influencing external eating behavior. We suggest that diminished insulin sensitivity correlates with activation in visual attention areas and inactivation in inhibitory control areas in healthy adolescents. Insulin resistance also associated with less consistency in implicit memory for a consumed meal, which may suggest lower ability to establish a dietary pattern, and can contribute to obesity. Differences in feeding behavior in SGA individuals were associated with insulin sensitivity and hippocampal alterations, suggesting that cognition and hormonal regulation are important components involved in their food intake modifications throughout life.

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Objective: To determine whether psychiatric and gaming pattern variables are associated with gaming disorder in a school-based sample. Methods: We analyzed data from the Brazilian High-Risk Cohort for Psychiatric Disorders, a community sample aged 10 to 18, using questionnaires on gaming use patterns. We applied the Gaming Addiction Scale to diagnose gaming disorder and the Development and Well-Being Behavior Assessment for other diagnoses. Results: Out of 407 subjects, 83 (20.4%) fulfilled the criteria for gaming disorder. More role-playing game players were diagnosed with gaming disorder that any other genre. Gaming disorder rates increased proportionally to the number of genres played. Playing online, being diagnosed with a mental disorder, and more hours of non-stop gaming were associated with higher rates of gaming disorder. When all variables (including age and gender) were considered in a logistic regression model, the number of genres played, the number of non-stop hours, the proportion of online games, and having a diagnosed mental disorder emerged as significant predictors of gaming disorder. Conclusion: Each variable seems to add further risk of gaming disorder among children and adolescents. Monitoring the length of gaming sessions, the number and type of genres played, time spent gaming online, and behavior changes may help parents or guardians identify unhealthy patterns of gaming behavior.

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Several stressors associated with the coronavirus disease 2019 (COVID-19) are expected to affect the mental health of global populations: the effects of physical distancing, quarantine, and social isolation; the emotional suffering of health and other frontline workers; neuropsychiatric sequelae in those affected by the virus; the impact to families of lives lost to the disease; differential effects for those with severe mental disorders; and the consequences of social and economic deterioration. In this context, we sought: to form a panel of Brazilian experts on child and adolescent health, neurodevelopment, health services, and adult and elderly mental health; and to compile evidence-based interventions to support suggested policy changes in Brazil to mitigate the expected increase in mental health disorders during the pandemic and its mental health consequences. The following actions are recommended: 1) invest in prevention programs for the safe return of students to schools; 2) adopt evidence-based psychosocial interventions to maintain an adequate environment for child and adolescent development; 3) target socially vulnerable populations and those experiencing discrimination; 4) train primary care teams to solve common mental health problems, provide needs-based assessments, and manage long-term, at-home care for older patients; 5) invest in technological advancements (e.g., telemedicine, e-Health, and web-based algorithms) to promote coordinated care; 6) increase access to and literacy in the use of computers and mobile phones, especially among older adults; 7) expand protocols for remote, brief psychotherapy interventions and psychoeducation to manage common mental health problems.

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OBJECTIVE: To determine whether psychiatric and gaming pattern variables are associated with gaming disorder in a school-based sample. METHODS: We analyzed data from the Brazilian High-Risk Cohort for Psychiatric Disorders, a community sample aged 10 to 18, using questionnaires on gaming use patterns. We applied the Gaming Addiction Scale to diagnose gaming disorder and the Development and Well-Being Behavior Assessment for other diagnoses. RESULTS: Out of 407 subjects, 83 (20.4%) fulfilled the criteria for gaming disorder. More role-playing game players were diagnosed with gaming disorder that any other genre. Gaming disorder rates increased proportionally to the number of genres played. Playing online, being diagnosed with a mental disorder, and more hours of non-stop gaming were associated with higher rates of gaming disorder. When all variables (including age and gender) were considered in a logistic regression model, the number of genres played, the number of non-stop hours, the proportion of online games, and having a diagnosed mental disorder emerged as significant predictors of gaming disorder. CONCLUSION: Each variable seems to add further risk of gaming disorder among children and adolescents. Monitoring the length of gaming sessions, the number and type of genres played, time spent gaming online, and behavior changes may help parents or guardians identify unhealthy patterns of gaming behavior.

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OBJECTIVE: The causes of high rates of psychological distress among health professionals and students are largely unknown. Health professionals respond to those who are in distress with empathy (feeling what others feel) or compassion (caring about what others feel). This study aims to investigate whether empathy and compassion are distinct traits and how both traits are associated with negative affect (burnout, depression, anxiety and anger symptoms) in undergraduate students and professionals in medicine, psychology and nursing. METHODS: A sample of 464 students and professionals filled out an online protocol with a sociodemographic data questionnaire and self-report questionnaires covering the variables of interest. RESULTS: The findings indicate that empathy is associated with higher negative affect, while compassion is associate with lower negative affect, which suggests that they are different traits. CONCLUSION: Our findings provide new evidence that the well-being of health professionals might be affected differently depending on socioemotional traits relevant to emotional connection.

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INTRODUCTION: Irritability has both mood and behavioral manifestations. These frequently co-occur, and it is unclear to what extent they are dissociable domains. We used confirmatory factor analysis and external validators to investigate the independence of mood and behavioral components of irritability. METHODS: The sample comprised 246 patients (mean age 45 years; 63% female) from four outpatient programs (depression, anxiety, bipolar, and schizophrenia) at a tertiary hospital. A clinical instrument rated by trained clinicians was specifically designed to capture irritable mood and disruptive behavior dimensionally, as well as current categorical diagnoses i.e., intermittent explosive disorder (IED); oppositional defiant disorder (ODD); and an adaptation to diagnose disruptive mood dysregulation disorder (DMDD) in adults. Confirmatory factor analysis (CFA) was used to test the best fitting irritability models and regression analyses were used to investigate associations with external validators. RESULTS: Irritable mood and disruptive behavior were both frequent, but diagnoses of disruptive syndromes were rare (IED, 8%; ODD, 2%; DMDD, 2%). A correlated model with two dimensions, and a bifactor model with one general dimension and two specific dimensions (mood and behavior) both had good fit indices. The correlated model had root mean square error of approximation (RMSEA) = 0.077, with 90% confidence interval (90%CI) = 0.071-0.083; comparative fit index (CFI) = 0.99; and Tucker-Lewis index (TLI) = 0.99, while the bifactor model had RMSEA = 0.041; CFI = 0.99; and TLI = 0.99 respectively). In the bifactor model, external validity for differentiation of the mood and behavioral components of irritability was also supported by associations between irritable mood and impairment and clinical measures of depression and mania, which were not associated with disruptive behavior. CONCLUSIONS: Psychometric and external validity data suggest both overlapping and specific features of the mood vs. disruptive behavior dimensions of irritability.

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Current first-line treatments for major depressive disorder (MDD) include pharmacotherapy and cognitive-behavioral therapy. However, one-third of depressed patients do not achieve remission after multiple medication trials, and psychotherapy can be costly and time-consuming. Although non-implantable neuromodulation (NIN) techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, electroconvulsive therapy, and magnetic seizure therapy are gaining momentum for treating MDD, the efficacy of non-convulsive techniques is still modest, whereas use of convulsive modalities is limited by their cognitive side effects. In this context, we propose that NIN techniques could benefit from a precision-oriented approach. In this review, we discuss the challenges and opportunities in implementing such a framework, focusing on enhancing NIN effects via a combination of individualized cognitive interventions, using closed-loop approaches, identifying multimodal biomarkers, using computer electric field modeling to guide targeting and quantify dosage, and using machine learning algorithms to integrate data collected at multiple biological levels and identify clinical responders. Though promising, this framework is currently limited, as previous studies have employed small samples and did not sufficiently explore pathophysiological mechanisms associated with NIN response and side effects. Moreover, cost-effectiveness analyses have not been performed. Nevertheless, further advancements in clinical trials of NIN could shift the field toward a more "precision-oriented" practice.

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