A developmental cascade model linking competence and symptoms was tested in a study of a normative, urban school sample of 205 children (initially 8 to 12 years old). Internalizing and externalizing symptoms and academic competence were assessed by multiple methods at the study outset and after 7, 10, and 20 years. A series of nested cascade models was tested through structural equation modeling. The final model indicated 2 hypothesized cascade effects: Externalizing problems evident in childhood appeared to undermine academic competence by adolescence, which subsequently showed a negative effect on internalizing problems in young adulthood. A significant exploratory effect was consistent with internalizing symptoms containing or lowering the net risk for externalizing problems under some conditions. These 3 cascade effects did not differ by gender and were not attributable to effects of IQ, parenting quality, or socioeconomic differences. Implications are discussed for developmental models of cascades, progressions, and preventive interventions.

Bullying and victimization were studied from a longitudinal, multi‐method, multi‐agent perspective as youngsters made the transition from primary through middle school. Generally, bullying and aggression increased with the transition to middle school and then declined. Bullying mediated youngsters' dominance status during the transition. Bullying may be one way in which young adolescents manage peer and dominance relationships as they make the transition into new social groups. Victimization declined from primary to secondary school. Correspondingly, youngsters' peer affiliations decreased, initially with the transition, and then recovered. Victimization, however, was buffered by peer affiliation, especially like most nominations relative to friendship nominations, during this time. Additionally, and consistent with the idea that bullying is used for dominance displays, cross‐sex comparisons of aggressive bouts indicated that boys targeted other boys and did not target girls. Results are discussed in terms of the changing functions of aggression during adolescence.

Home baseline and laboratory stressor (Trier Social Stress Test for Children) measures of salivary cortisol were obtained from 82 participants (40 girls) aged 9, 11, 13, and 15 years. Measures of pubertal development, self-reported stress, parent reports of child depressive symptoms and fearful temperament, and cardiac measures of sympathetic and parasympathetic activity were also obtained. Significant increases in the home cortisol baselines were found with age and pubertal development. Cortisol stress reactivity differed by age group with 11-year-olds and 13-year-old boys showing blunted reactivity and 9-year-olds, 13-year-old girls, and 15-year-olds showing significant cortisol reactions. Cortisol reactivity correlated marginally with sexual maturation. Measures of sympathetic activity revealed increased sympathetic modulation with age. Higher sympathetic tone was associated with more fearful temperament, whereas greater cortisol reactivity was associated with more anxious and depressed symptoms for girls. The importance of these findings for the hypothesis that puberty-associated increases in hypothalamic–pituitary–adrenal axis activity heightens the risk of psychopathology is discussed.

Variable, glutamine-encoding, CAA interruptions indicate that a property of the uninterrupted HTT CAG repeat sequence, distinct from the length of huntingtin's polyglutamine segment, dictates the rate at which Huntington's disease (HD) develops. The timing of onset shows no significant association with HTT cis-eQTLs but is influenced, sometimes in a sex-specific manner, by polymorphic variation at multiple DNA maintenance genes, suggesting that the special onset-determining property of the uninterrupted CAG repeat is a propensity for length instability that leads to its somatic expansion. Additional naturally occurring genetic modifier loci, defined by GWAS, may influence HD pathogenesis through other mechanisms. These findings have profound implications for the pathogenesis of HD and other repeat diseases and question the fundamental premise that polyglutamine length determines the rate of pathogenesis in the "polyglutamine disorders."

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.