Externalizing outcomes in high risk youth

项目概要/摘要 公众的注意力往往被吸引到暴力与心理健康问题之间的关系， 暴力行为是一种非常复杂的现象，受到无数社会、心理、环境 和生物影响。本项目旨在调查这些影响中的一个大样本， 对以前被监禁的青年进行跟踪，并收集宝贵的纵向数据。我们的团队 我与这些年轻人和他们的家人一起收集了详细的心理、行为和神经成像 作为以前NIH资助的调查的一部分。目前的项目旨在重新评估这些 个体（现在是年轻的成年人）检查长程阳性（即，停止吸毒/反社会 行为）和负面（药物复发，反社会行为）结果。我们会收集新的神经成像 扫描，结合先前的MRI数据，将有助于量化的变化轨迹，映射到 坚持和停止将结果外化。先进的机器学习方法将是 与结构、功能、网络和动态网络脑测量结合使用， 行为和心理测量。机器学习方法能够识别 高维数据，并描绘出最能预测特定 结果变量。使用这些方法，我们打算定义预测结果的神经机制。我们 还旨在确定变量的组合，赋予更大的风险，持续的反社会行为， 暴力这项工作的翻译价值将是澄清数据的信息模式， 可预防的结果。此外，指示特定脆弱性的神经测量将被确定为 具体的治疗目标和新的干预战略。通过识别特定的漏洞， 伴随积极成果的变化，我们将更接近于理解认识和 防止代价高昂的暴力行为

Project Summary/abstract Public attention has often been drawn to the relationship between violence and mental health issues, but violent behavior is a very complex phenomenon undergirded by myriad social, psychological, environmental, and biological influences. This project intends to investigate these influences among a large sample of previously incarcerated youth, following up with them and collecting valuable longitudinal data. Our team worked with these youth and their families collecting detailed psychological, behavioral, and neuroimaging measures as part of a previous NIH-funded investigation. The current project aims to re-assess these individuals (now young adults) to examine long-range positive (i.e., desistance from drug use/antisocial behavior) and negative (relapse to drugs, antisocial behavior) outcomes. We will collect new neuroimaging scans, which combined with prior MRI data, will be useful for quantifying trajectories of change that map to persistence and desistence from externalizing outcomes. Advanced machine-learning approaches will be utilized in conjunction with structural, functional, network, and dynamic network brain measures in addition to behavioral and psychological measures. Machine learning approaches are capable of identifying patterns in high-dimensional data and delineating the unique combinations of variables that are most predictive of specific outcome variables. Using these methods, we intend to define neural mechanisms that predict outcomes. We also aim to identify combinations of variables that confer greater risk for persistent antisocial behavior and violence. The translational value of this work will be to clarify informative patterns of data that may indicate preventable outcomes. Furthermore, neural measures indicative of specific vulnerability will be identified as specific targets for treatment and novel intervention strategies. By identifying specific vulnerabilities and the changes that accompany positive outcomes, we will be closer to understanding the best way to recognize and prevent costly violent behavior.