Navegando por Palavras-chave "Mental-Disorders"

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    Conditional risk for posttraumatic stress disorder in an epidemiological study of a Brazilian urban population
    (Pergamon-Elsevier Science Ltd, 2016) Luz, Mariana Pires; Coutinho, Evandro S. F.; Berger, William; Mendlowicz, Mauro V.; Vilete, Liliane M. P.; Mello, Marcelo F. [UNIFESP]; Quintana, Maria Ines [UNIFESP]; Bressan, Rodrigo A. [UNIFESP]; Andreoli, Sergio B. [UNIFESP]; Mari, Jair J. [UNIFESP]; Figueira, Ivan
    Introduction: Conditional risk for PTSD is the risk of developing PTSD after exposure to traumatic events. This epidemiological study of the general urban population from the two largest cities in Brazil reports exposure to traumatic events conditional risk for PTSD and proportion/estimated number of PTSD cases secondary to each type of traumatic event. Method: Cross-sectional study of general population (15-75 y.o.) from Rio de Janeiro and Sao Paulo. PTSD was assessed through Composite International Diagnostic Interview 2.1. Results: Our main findings, from 3744 participants, were: 1) high prevalence of traumatic events (86%), urban violence being the most common 2) conditional risk for PTSD was 11.1% 3) women (15.9%) have overall conditional risk 3 times higher than men (5.1%) 4) war-related trauma (67.8%), childhood sexual abuse (49.1%) and adult sexual violence (44.1%) had the highest conditional risks 5) 35% of PTSD cases (estimated 435,970 individuals) were secondary to sudden/unexpected death of a close person, and 40% secondary to interpersonal violence. Conclusions: Brazilian urban population is highly exposed to urban violence, and overall conditional risk for PTSD was 11.1%. Violence prevention and enhancement of resilience should be part of public policies, and mental health sequelae of trauma should be better recognized and treated.
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    Conditional risk for posttraumatic stress disorder in an epidemiological study of a Brazilian urban population
    (Pergamon-Elsevier Science Ltd, 2016) Luz, Mariana Pires; Coutinho, Evandro S. F.; Berger, William; Mendlowicz, Mauro V.; Vilete, Liliane M. P.; Mello, Marcelo F. [UNIFESP]; Quintana, Maria Ines [UNIFESP]; Bressan, Rodrigo A. [UNIFESP]; Andreoli, Sergio B. [UNIFESP]; Mari, Jair J. [UNIFESP]; Figueira, Ivan
    Introduction: Conditional risk for PTSD is the risk of developing PTSD after exposure to traumatic events. This epidemiological study of the general urban population from the two largest cities in Brazil reports exposure to traumatic events
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    Default mode network maturation and psychopathology in children and adolescents
    (Wiley, 2016) Sato, Joao Ricardo [UNIFESP]; Salum, Giovanni Abrahao; Gadelha, Ary| [UNIFESP]; Vieira, Gilson; Manfro, Gisele Gus; Zugman, Andre [UNIFESP]; Picon, Felipe Almeida; Pan, Pedro Mario [UNIFESP]; Hoexter, Marcelo Queiroz [UNIFESP]; Anes, Mauricio; Moura, Luciana Monteiro [UNIFESP]; Gomes Del'Aquilla, Marco Antonio [UNIFESP]; Amaro, Edson, Jr.; McGuire, Philip; Tavares Lacerda, Acioly Luiz [UNIFESP]; Rohde, Luis Augusto; Miguel, Euripedes Constantino; Jackowski, Andrea Parolin [UNIFESP]; Bressan, Rodrigo Affonseca [UNIFESP]; Crossley, Nicolas
    BackgroundThe human default mode (DMN) is involved in a wide array of mental disorders. Current knowledge suggests that mental health disorders may reflect deviant trajectories of brain maturation. MethodWe studied 654 children using functional magnetic resonance imaging (fMRI) scans under a resting-state protocol. A machine-learning method was used to obtain age predictions of children based on the average coefficient of fractional amplitude of low frequency fluctuations (fALFFs) of the DMN, a measure of spontaneous local activity. The chronological ages of the children and fALFF measures from regions of this network, the response and predictor variables were considered respectively in a Gaussian Process Regression. Subsequently, we computed a network maturation status index for each subject (actual age minus predicted). We then evaluated the association between this maturation index and psychopathology scores on the Child Behavior Checklist (CBCL). ResultsOur hypothesis was that the maturation status of the DMN would be negatively associated with psychopathology. Consistent with previous studies, fALFF significantly predicted the age of participants (p<.001). Furthermore, as expected, we found an association between the DMN maturation status (precocious vs. delayed) and general psychopathology scores (p=.011). ConclusionsOur findings suggest that child psychopathology seems to be associated with delayed maturation of the DMN. This delay in the neurodevelopmental trajectory may offer interesting insights into the pathophysiology of mental health disorders.
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    Default mode network maturation and psychopathology in children and adolescents
    (Wiley, 2016) Sato, Joao Ricardo [UNIFESP]; Salum, Giovanni Abrahao; Gadelha, Ary| [UNIFESP]; Vieira, Gilson; Manfro, Gisele Gus; Zugman, Andre [UNIFESP]; Picon, Felipe Almeida; Pan, Pedro Mario [UNIFESP]; Hoexter, Marcelo Queiroz [UNIFESP]; Anes, Mauricio; Moura, Luciana Monteiro [UNIFESP]; Gomes Del'Aquilla, Marco Antonio [UNIFESP]; Amaro Junior, Edson; McGuire, Philip; Lacerda, Acioly Luiz Tavares de [UNIFESP]; Rohde, Luis Augusto; Miguel, Euripedes Constantino; Jackowski, Andrea Parolin [UNIFESP]; Bressan, Rodrigo Affonseca [UNIFESP]; Crossley, Nicolas; Universidade Federal de São Paulo (UNIFESP)
    BackgroundThe human default mode (DMN) is involved in a wide array of mental disorders. Current knowledge suggests that mental health disorders may reflect deviant trajectories of brain maturation. MethodWe studied 654 children using functional magnetic resonance imaging (fMRI) scans under a resting-state protocol. A machine-learning method was used to obtain age predictions of children based on the average coefficient of fractional amplitude of low frequency fluctuations (fALFFs) of the DMN, a measure of spontaneous local activity. The chronological ages of the children and fALFF measures from regions of this network, the response and predictor variables were considered respectively in a Gaussian Process Regression. Subsequently, we computed a network maturation status index for each subject (actual age minus predicted). We then evaluated the association between this maturation index and psychopathology scores on the Child Behavior Checklist (CBCL). ResultsOur hypothesis was that the maturation status of the DMN would be negatively associated with psychopathology. Consistent with previous studies, fALFF significantly predicted the age of participants (p<.001). Furthermore, as expected, we found an association between the DMN maturation status (precocious vs. delayed) and general psychopathology scores (p=.011). ConclusionsOur findings suggest that child psychopathology seems to be associated with delayed maturation of the DMN. This delay in the neurodevelopmental trajectory may offer interesting insights into the pathophysiology of mental health disorders.
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    Positive Attributes Buffer the Negative Associations Between Low Intelligence and High Psychopathology With Educational Outcomes
    (Elsevier Science Inc, 2016) Hoffmann, Mauricio Scope; Leibenluft, Ellen; Stringaris, Argyris; Laporte, Paola Paganella; Pan, Pedro Mario [UNIFESP]; Gadelha, Ary [UNIFESP]; Manfro, Gisele Gus; Miguel, Euripedes Constantino [UNIFESP]; Rohde, Luis Augusto; Salum, Giovanni Abrahao
    Objective: This study examines the extent to which children's positive attributes are distinct from psychopathology. We also investigate whether positive attributes change or "buffer" the impact of low intelligence and high psychopathology on negative educational outcomes. Method: In a community sample of 2,240 children (6-14 years of age), we investigated associations among positive attributes, psychopathology, intelligence, and negative educational outcomes. Negative educational outcomes were operationalized as learning problems and poor academic performance. We tested the discriminant validity of psychopathology versus positive attributes using confirmatory factor analysis (CFA) and propensity score matching analysis (PSM), and used generalized estimating equations (GEE) models to test main effects and interactions among predictors of educational outcomes. Results: According to both CFA and PSM, positive attributes and psychiatric symptoms were distinct constructs. Positive attributes were associated with lower levels of negative educational outcomes, independent of intelligence and psychopathology. Positive attributes buffer the negative effects of lower intelligence on learning problems, and higher psychopathology on poor academic performance. Conclusion: Children's positive attributes are associated with lower levels of negative school outcomes. Positive attributes act both independently and by modifying the negative effects of low intelligence and high psychiatric symptoms on educational outcomes. Subsequent research should test interventions designed to foster the development of positive attributes in children at high risk for educational problems.
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    Positive Attributes Buffer the Negative Associations Between Low Intelligence and High Psychopathology With Educational Outcomes
    (Elsevier Science Inc, 2016) Hoffmann, Mauricio Scope; Leibenluft, Ellen; Stringaris, Argyris; Laporte, Paola Paganella; Pan, Pedro Mario [UNIFESP]; Gadelha, Ary [UNIFESP]; Manfro, Gisele Gus; Miguel, Euripedes Constantino [UNIFESP]; Rohde, Luis Augusto; Salum, Giovanni Abrahao
    Objective: This study examines the extent to which children's positive attributes are distinct from psychopathology. We also investigate whether positive attributes change or "buffer" the impact of low intelligence and high psychopathology on negative educational outcomes. Method: In a community sample of 2,240 children (6-14 years of age), we investigated associations among positive attributes, psychopathology, intelligence, and negative educational outcomes. Negative educational outcomes were operationalized as learning problems and poor academic performance. We tested the discriminant validity of psychopathology versus positive attributes using confirmatory factor analysis (CFA) and propensity score matching analysis (PSM), and used generalized estimating equations (GEE) models to test main effects and interactions among predictors of educational outcomes. Results: According to both CFA and PSM, positive attributes and psychiatric symptoms were distinct constructs. Positive attributes were associated with lower levels of negative educational outcomes, independent of intelligence and psychopathology. Positive attributes buffer the negative effects of lower intelligence on learning problems, and higher psychopathology on poor academic performance. Conclusion: Children's positive attributes are associated with lower levels of negative school outcomes. Positive attributes act both independently and by modifying the negative effects of low intelligence and high psychiatric symptoms on educational outcomes. Subsequent research should test interventions designed to foster the development of positive attributes in children at high risk for educational problems.