Externalizing outcomes in high risk youth

高危青少年的外化结果

• 批准号: 10153459
• 负责人: KENT A KIEHL
• 金额: $ 69.21万
• 依托单位: THE MIND RESEARCH NETWORK
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-05 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10153459
• 关键词: Academia; Adolescence; Adolescent; Adult; Age of Onset; Aggressive behavior; Alcohols; Anterior; Area; Attention; Behavior; Behavioral; Biological; Brain; Brain imaging; Collaborations; Collection; Complex; Consent; Crime; Criminology; Dangerousness; Data; Data Set; Development; Discipline; Drug usage; Evaluation; Event; Expenditure; Family; Firearms; Follow-Up Studies; Forensic Medicine; Funding; Future; Gender; Health; Health Status; Healthcare; Image; Impairment; Imprisonment; Individual; Intervention; Interview; Investigation; Knowledge; Lateral; Lead; Life; Life Cycle Stages; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Medial; Mental Health; Methods; Modeling; Neurocognitive; Neuropsychological Tests; New Mexico; Outcome; Participant; Pathologic; Pattern; Pharmaceutical Preparations; Property; Psychological Factors; Psychosocial Factor; Relapse; Research; Rest; Risk; Sampling; Scanning; Security; Sociology; Statistical Models; Structure; Substance abuse problem; Sum; Techniques; Time; United States; United States National Institutes of Health; Violence; Work; Youth; anti social; antisocial behavior; behavioral outcome; callous unemotional trait; cognitive control; cognitive neuroscience; cohort; convict; correctional system; cost; developmental psychology; efficacious treatment; externalizing behavior; follow-up; high dimensionality; high risk; indexing; multidimensional data; multimodal data; neural circuit; neurodevelopment; neuroimaging; neuromechanism; novel; outcome prediction; pediatric trauma; predictive modeling; prevent; prospective; protective factors; psychologic; psychosocial; relating to nervous system; repeat offender; social; social factors; substance use; trait impulsivity; treatment strategy; young adult

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.

项目摘要/摘要 暴力和心理健康问题之间的关系经常引起公众的注意，但 暴力行为是一种非常复杂的现象，有无数的社会、心理、环境、 以及生物因素的影响。该项目打算在大样本中调查这些影响 对以前被监禁的青年进行跟踪，并收集有价值的纵向数据。我们队 与这些年轻人及其家人一起收集详细的心理、行为和神经成像 这些措施是之前NIH资助的调查的一部分。目前的项目旨在重新评估这些 检查长期阳性(即戒毒/反社会)的个人(现为年轻人) 行为)和负面(吸毒、反社会行为)结果。我们将收集新的神经成像 结合以前的MRI数据进行扫描，将有助于量化映射到 坚持不懈和坚持不懈地将结果外化。先进的机器学习方法将是 与结构、功能、网络和动态网络大脑测量相结合使用 行为和心理测量。机器学习方法能够识别模式 高维数据和描绘最具预测性的特定变量的独特组合 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 还旨在确定具有更大风险的持续反社会行为的变量组合，以及 暴力。这项工作的翻译价值将是澄清数据的信息模式，这些模式可能表明 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 治疗的具体目标和新的干预策略。通过识别特定的漏洞和 伴随着积极结果而来的变化，我们将更接近于理解认识和 防止代价高昂的暴力行为。