CDI-Type II: Computational Tools for Behavioral Analysis, Diagnosis, and Intervention of at Risk Children

该项目将开发算法，协助对有行为障碍风险的儿童进行早期诊断。先前的研究表明，用于识别高危儿童的行为调查的两个关键领域是运动活动和情绪范围表现的异常，尤其是面部。运动异常是基于对运动控制涉及与多巴胺相关的大脑回路的观察；这些也与行为障碍有关。许多不同的疾病都观察到情绪范围调节的中断，因此面部表情被包括在研究中。迄今为止，对运动和情绪范围的评估都是由观看和评价个人视频的专家完成的。然而，这些专家的主观评分限制了对行为条件的分析，只局限于行为的一个狭窄范围，只适用于个体受试者的一小部分人群，而且既昂贵又依赖于观察者的特定专业知识。为了进行更广泛的人群筛查，需要自动化。获取和量化专家专业知识的创新方法将伴随着评估行为演变的指标。此外，新的计算工具将支持评估干预措施的有效性。拟议工作的更广泛影响将涉及通过提供一种系统的方法来加强早期发现、预防或减轻行为障碍，并可能降低漏检或晚诊断的长期成本，从而改善整个人群的心理健康水平。通过将项目主题纳入明尼苏达大学理工学院、医学院和教育与人类发展学院的课程，并创建一个关于知识发现和行为分析的年度研讨会、教程、网页和wiki的项目，研究结果将与教育过程相结合。该团队将在巴肯博物馆为儿童开发一个互动式展览，并为儿童发展研究所和类似机构的实习教师制作新的教学材料。中央网络存储库的开发将确保算法和数据可以随时用于适当的研究。

This project will develop algorithms to assist with the early diagnosis of children who are at risk of developing behavioral disorders. Previous research has indicated that two critical areas of behavioral investigation for use in identifying at-risk children have been abnormalities in motor activities and emotional range displays, especially of the face. Motor abnormalities are based on the observation that motor control involves the circuits of the brain associated with dopamine; these are also implicated in behavioral disorders. Many different disorders share the observation of disruption in the emotional range regulation, so facial expressions are included in the study.To date, assessments of motor and emotional range have been done by the experts who view and rate videos of an individual. However, these expert, subjective ratings limit the analysis of behavioral conditions to only a narrow range of behaviors, work only for small populations of individual subjects, and are both costly and dependent on the observer's particular expertise. In order to enable wider population screening, automation is required. Innovative ways of capturing and quantifying the expertise of experts will be accompanied by metrics for assessing the evolution of the behavior. In addition, new computational tools will support evaluation of the effectiveness of interventions.The broader impacts of the proposed work will involve improved mental health levels across the populations by providing a systematic approach for enhancing early detection, prevention, or mitigation of behavioral disorders and likely reduce the long-term costs of missed or late diagnosis. The research results will be blended with the educational process through inclusion of project themes in the curricula at the Institute of Technology, the Medical School, and the College of Education and Human Development at the University of Minnesota and the creation of a program with annual workshops, tutorials, web pages and a wiki on knowledge discovery and behavioral analysis. The team will develop an interactive exhibit for children at the Bakken Museum, and create of new instructional material for student teachers at the Institute of Child Development and similar institutions. Development of a central web repository will insure that the algorithms and the data will be readily available for appropriate research.