Faculty Bibliography

Local translation can support memory consolidation by supplying new proteins to synapses undergoing plasticity. Translation in adult forebrain dendrites is an established mechanism of synaptic plasticity and is regulated by learning, yet there is no evidence for learning-regulated protein synthesis in adult forebrain axons, which have traditionally been believed to be incapable of translation. Here we show that axons in the adult rat amygdala contain translation machinery, and use translating ribosome affinity purification (TRAP) with RNASeq to identify mRNAs in cortical axons projecting to the amygdala, over 1200 of which were regulated during consolidation of associative memory. Mitochondrial and translation-related genes were upregulated, whereas synaptic, cytoskeletal, and myelin-related genes were downregulated; the opposite effects were observed in the cortex. Our results demonstrate that axonal translation occurs in the adult forebrain and is altered after learning, supporting the likelihood that local translation is more a rule than an exception in neuronal processes.

Large-scale functional connectome formation and re-organization is apparent in the second trimester of pregnancy, making it a crucial and vulnerable time window in connectome development. Here we identified which architectural principles of functional connectome organization are initiated prior to birth, and contrast those with topological characteristics observed in the mature adult brain. A sample of 105 pregnant women participated in human fetal resting-state fMRI studies (fetal gestational age between 20 and 40 weeks). Connectome analysis was used to analyze weighted network characteristics of fetal macroscale brain wiring. We identified efficient network attributes, common functional modules and high overlap between the fetal and adult brain network. Our results indicate that key features of the functional connectome are present in the second and third trimesters of pregnancy. Understanding the organizational principles of fetal connectome organization may bring opportunities to develop markers for early detection of alterations of brain function.SIGNIFICANCE STATEMENTThe fetal to neonatal period is well known as a critical stage in brain development. Rapid neurodevelopmental processes establish key functional neural circuits of the human brain. Prenatal risk factors may interfere with early trajectories of connectome formation and thereby shape future health outcomes. Recent advances in MRI have made it possible to examine fetal brain functional connectivity. In this study, we evaluate the network topography of normative functional network development during connectome genesis in utero Understanding the developmental trajectory of brain connectivity provides a basis for understanding how the prenatal period shapes future brain function and disease dysfunction.

Although electrophysiological (electroencephalography) measures of executive functions (e.g. error monitoring) have been used to predict academic achievement in typically developing children, work investigating a link between error monitoring and academic skills in children with autism spectrum disorder is limited. In this study, we employed traditional electrophysiological and advanced time-frequency methods, combined with principal component analyses, to extract neural activity related to error monitoring and tested their relations to academic achievement in cognitively able kindergarteners with autism spectrum disorder. In total, 35 cognitively able kindergarteners with autism spectrum disorder completed academic assessments and the child-friendly "Zoo Game" Go/No-go task at school entry. The Go/No-go task successfully elicited an error-related negativity and error positivity in children with autism spectrum disorder as young as 5 years at fronto-central and posterior electrode sites, respectively. We also observed increased response-related theta power during errors relative to correct trials at fronto-central sites. Both larger error positivity and theta power significantly predicted concurrent academic achievement after controlling for behavioral performance on the Zoo Game and intelligence quotient. These results suggest that the use of time-frequency electroencephalography analyses, combined with traditional event-related potential measures, may provide new opportunities to investigate neurobiological mechanisms of executive function and academic achievement in young children with autism spectrum disorder.

Gender is one of the central categories organizing children's social world. Clear patterns of gender development have been well-documented among cisgender children (i.e., children who identify as a gender that is typically associated with their sex assigned at birth). We present a comprehensive study of gender development (e.g., gender identity and gender expression) in a cohort of 3- to 12-y-old transgender children (n = 317) who, in early childhood, are identifying and living as a gender different from their assigned sex. Four primary findings emerged. First, transgender children strongly identify as members of their current gender group and show gender-typed preferences and behaviors that are strongly associated with their current gender, not the gender typically associated with their sex assigned at birth. Second, transgender children's gender identity (i.e., the gender they feel they are) and gender-typed preferences generally did not differ from 2 comparison groups: cisgender siblings (n = 189) and cisgender controls (n = 316). Third, transgender and cisgender children's patterns of gender development showed coherence across measures. Finally, we observed minimal or no differences in gender identity or preferences as a function of how long transgender children had lived as their current gender. Our findings suggest that early sex assignment and parental rearing based on that sex assignment do not always define how a child identifies or expresses gender later.

This tutorial introduces recent developments in precision medicine for estimating treatment decision rules. The objective of these developments is to advance personalised healthcare by identifying an optimal treatment option for each individual patient based on each patient's characteristics. The methods detailed in this tutorial define composite variables from the patient measures that can be viewed as 'biosignatures' for differential treatment response, which we have termed 'generated effect modifiers'. In contrast to most machine learning approaches to precision medicine, these biosignatures are derived from linear and non-linear regression models and thus have the advantage of easy visualisation and ready interpretation. The methods are illustrated using examples from randomised clinical trials.

Objective: Previous research in adults with ADHD showed high rates of obesity and unhealthy food choices. There is evidence that contextual cues, for example, advertisements, influence food choices. This study assessed the sensitivity of university students with ADHD to advertised food. Method: University students (N = 457) with and without ADHD participated in a cafeteria field experiment. Food choices were examined in periods of advertising either healthy or unhealthy sandwiches. Results: Students with ADHD (a) chose less healthy food items, (b) were more influenced by advertising, (c) showed the same overall healthy food choices as controls when exposed to healthy advertising. Conclusion: Students with ADHD chose unhealthier foods at the cafeteria but were also more influenced by advertising. Healthy food advertisements raised their healthy food choices. As this population has strong association with unhealthy dietary patterns, it is important to investigate the influence of food cues on their eating habits.

Background/UNASSIGNED:(NICHD) funded, cluster randomized-controlled trial to evaluate a combination intervention, titled Suubi + Adherence, aimed at improving ART adherence among HIV perinatally infected adolescents (ages 10-16 at study enrollment) in Uganda. Methods/UNASSIGNED:Suubi + Adherence was evaluated via a two-arm cluster randomized-controlled trial design in 39 health clinics, with a total enrollment of 702 HIV + adolescents (ages 10-16 at enrollment). The study addresses two primary outcomes: 1) adherence to HIV treatment regimen and 2) HIV knowledge and attitudes. Secondary outcomes include family functioning, sexual risk-taking behavior, and financial savings behavior. For potential scale-up, cost effectiveness analysis was employed to compare the relative costs and outcomes associated with each study arm: family economic strengthening comprising matched savings accounts, financial management training and small business development, all intended for family economic security versus bolstered usual care (SOC) comprising enhanced adherence sessions to ensure more standardized and sufficient adherence counseling. Discussion/UNASSIGNED:This study aims to advance knowledge and inform the development of the next generation of programs aimed at increasing adherence to HIV treatment for HIV + adolescents in low-resource regions such as SSA. To our knowledge, the proposed study is the first to integrate and test family economic empowerment and stability-focused interventions for HIV + adolescents in Uganda (and much of SSA)-so families would have the necessary finances to manage HIV/AIDS as a chronic illness. The study would provide crucial evidence about the effects of an economic empowerment program on short and long-term impact, which is essential if such interventions are to be taken to scale. Trial registration/UNASSIGNED:This trial was registered with ClinicalTrials.gov (registration number: NCT01790373) on 13 February 2013.

PURPOSE/OBJECTIVE:Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. METHODS:Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. RESULTS:The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene. CONCLUSION/CONCLUSIONS:Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.