Suchergebnisse

AbstractBehavioral and molecular genetic research has established that child cognitive ability and academic performance are substantially heritable, but genetic variation does not account for all of the stratification of cognitive and academic outcomes across families. Which specific contexts and experiences contribute to these shared environmental influences on cognitive ability and academic achievement? Using an ethnically and socioeconomically diverse sample of N = 1728 twins ages 7–20 from the Texas Twin Project, we identified specific measured family, school, and neighborhood socioecological contexts that statistically accounted for latent shared environmental variance in cognitive abilities and academic skills. Composite measures of parent socioeconomic status (SES), school demographic composition, and neighborhood SES accounted for moderate proportions of variation in IQ and achievement. Total variance explained by the multilevel contexts ranged from 15% to 22%. The influence of family SES on IQ and achievement overlapped substantially with the influence of school and neighborhood predictors. Together with race, the measured socioecological contexts explained 100% of shared environmental influences on IQ and approximately 79% of shared environmental influences on both verbal comprehension and reading ability. In contrast, nontrivial proportions of shared environmental variation in math performance were left unexplained. We highlight the potential utility of constructing "polyenvironmental risk scores" in an effort to better predict developmental outcomes and to quantify children's and adolescents' interrelated networks of experiences. A video abstract of this article can be viewed at: https://youtu.be/77E_DctFsr0

AbstractTourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method – support vector machine (SVM) classification – to test whether patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI, could predict diagnostic group membership for individuals. RSFC data from 42 children with TS (8–15 yrs) and 42 unaffected controls (age, IQ, in‐scanner movement matched) were included. While univariate tests identified no significant group differences, SVM classified group membership with ~70% accuracy (p < .001). We also report a novel adaptation of SVM binary classification that, in addition to an overall accuracy rate for the SVM, provides a confidence measure for the accurate classification of each individual. Our results support the contention that multivariate methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with TS.

This study aimed to examine changes in depression and anxiety symptoms from before to during the first 6 months of the COVID‐19 pandemic in a sample of 1,339 adolescents (9–18 years old, 59% female) from three countries. We also examined if age, race/ethnicity, disease burden, or strictness of government restrictions moderated change in symptoms. Data from 12 longitudinal studies (10 U.S., 1 Netherlands, 1 Peru) were combined. Linear mixed effect models showed that depression, but not anxiety, symptoms increased significantly (median increase = 28%). The most negative mental health impacts were reported by multiracial adolescents and those under 'lockdown' restrictions. Policy makers need to consider these impacts by investing in ways to support adolescents' mental health during the pandemic.