GENERATIVE MODELS FOR LARGE-SCALE SIMULATIONS OF CONNECTOME DEVELOPMENT.

Functional interactions and anatomic connections between brain regions form the connectome. Its mathematical representation in terms of a graph reflects the inherent neuroanatomical organization into structures and regions (nodes) that are interconnected through neural fiber tracts and/or interact functionally (edges). Without knowledge of the ground truth topology of the connectome, functional (directional or nondirectional) graphs represent estimates of signal correlations, from which underlying mechanisms and processes, such as development and aging, or neuropathologies, are difficult to unravel.

Strength and resilience of developing brain circuits predict adolescent emotional and stress responses during the COVID-19 pandemic.

The COVID-19 pandemic has had profound but incompletely understood adverse effects on youth. To elucidate the role of brain circuits in how adolescents responded to the pandemic's stressors, we investigated their prepandemic organization as a predictor of mental/emotional health in the first ~15 months of the pandemic. We analyzed resting-state networks from n = 2,641 adolescents [median age (interquartile range) = 144.

HIV/Pre-Exposure Prophylaxis Knowledge and Acceptability of Rapid HIV Testing Among Transgender Adolescents in a Multidisciplinary Gender Clinic.

Purpose: The objectives of this study were to assess the knowledge of HIV and pre-exposure prophylaxis (PrEP) in transgender adolescents and young adults (AYAs) and to test the acceptability of rapid HIV testing among transgender adolescents in a multidisciplinary gender clinic.

Methods: Participants enrolled on the same day as their mental health or medical appointment in a multidisciplinary gender clinic. They completed survey questions regarding HIV and PrEP knowledge and were also offered an optional same-day, rapid, fourth-generation HIV test.

Community detection in the human connectome: Method types, differences and their impact on inference.

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood.