ADHD heterogeneity, mechanisms, and risk profile

ADHD仍然是一个严重的公共卫生问题，特别是在长期结果方面， 跟踪其他项目的数据，如MTA研究，表明目前的评估和 持续的治疗实践不会改变临床过程。此外，社会和临床问题 仍然是关于错误识别多动症儿童谁需要照顾，因为有些孩子似乎 在发展上有所改善，而其他人的结果很差。虽然人们对这一点了解很多， 相关的发展过程中，这种知识很少转化为临床实践，并在 特别是临床预测-基本上，决定一个孩子，表现为多动症， 干预或临床医生可以等待和观察的人。我们之前已经取得了进展 在这里，通过将ADHD重新概念化为涉及认知控制的自我调节问题， 情绪调节和情绪性。这有助于我们找到新的和有效的子配置文件， 预测。我们在这里跟进，并增加新的努力，使用先进的计算工具来预测 在7-19岁的发育期内的临床过程中，使用一系列不同的测量方法， 水平的分析，从而开发算法，可以指向下一代临床 纵向研究的翻译。 相关性（参见说明）： ADHD仍然是数百万儿童的严重和损害性综合征，影响家庭，学校， 和生活质量。虽然人们对ADHD的相关因素了解很多，但这些信息还没有 充分转化为更有效的临床决策， 在这个人口中。该项目旨在使用高级分析和长期跟踪数据来识别 更好的方法来识别具有特征性特征的ADHD儿童群体，以改善临床护理。

ADHD remains a serious public health concern particularly in relation to long-term outcome, with recent follow up data from other projects, such as the MTA study, suggesting that current assessment and sustained treatment practices do not much alter clinical course. In addition, societal and clinical concerns remain about mis-identification of children with ADHD who need care, because some children seem to improve developmentally, while others have poor outcomes. While a great deal is known about the correlates of developmental course, little of this knowledge has translated to clinical practice, and in particular to clinical prediction—essentially, deciding whether a child, presenting with ADHD, requires intervention or for whom a clinician can afford to wait and observed. We have previously made progress here by re-conceptualizing ADHD as a problem in self-regulation involving cognitive control as well as emotion regulation and emotionality. This has helped us find new and valid sub-profiles that are clinically predictive. We follow that up here and add new effort to use advanced computational tools to predict clinical course over a developmental period from 7-19 years of age, using a range of measures at different levels of analysis, and thus to develop algorithms that could point the way toward next-generation clinical translation from longitudinal studies. RELEVANCE (See instructions): ADHD continues to be a serious and impairing syndrome for millions of children, affecting families, schools, and life quality. While a great deal is known about the correlates of ADHD, this information has not translated sufficiently into more efficient clinical decision-making that recognizes the variety of children within this population. This project seeks to use advanced analytics and long-term follow up data to identify better ways to identify groups of children with ADHD with characteristic profiles to improve clinical care.