CHS: Large: Collaborative Research: Computational Science for Improving Assessment of Executive Function in Children

儿童早期认知障碍的识别为成功的补救干预提供了最好的机会，因为大脑的可塑性随着年龄的增长而减弱。注意缺陷多动障碍（ADHD）是一种精神神经发育障碍，很难诊断或与其他障碍区分开来。症状包括注意力不集中、多动或行为冲动，所有这些都经常导致在学校表现不佳，并在以后的生活中持续存在。在这个项目中，一个由计算机和神经认知科学家组成的跨学科团队将开发和实施变革性的计算方法来评估幼儿的认知特征并解决这些问题。该项目将利用已经在美国300所学校进行的物理和计算机练习，涉及数千名儿童，其中许多人被诊断患有多动症或其他学习障碍。项目成果将对孩子在学校的成功、自我形象、未来的就业和社区功能产生重要影响。pi将发现关于体育锻炼在认知训练中的作用的新知识，包括个人指标与随时间推移的改善程度之间的相关性。他们将确定认知科学家目前未知的重要新度量和相关性，这也将对其他应用领域产生广泛影响。pi将为神经认知专家开发一门关于计算认知科学的跨学科课程和一门关于用户界面的课程。这项研究将涉及四个重点。pi将设计新的人体运动分析和计算机视觉算法，可以自动评估结构化体育活动中的具身认知，这将构成提高幼儿认知评估准确性和效率的突破。智能挖掘技术将用于发现关于体育锻炼在认知训练中的作用的新知识，并发现个体指标与随时间推移的改善程度之间的相关性。将开发一种方法，使用先进的多模态传感来收集和处理大量基于证据的评估数据，并使用智能机制来了解儿童的执行功能能力，并帮助发现认知功能障碍的可能原因。我们将设计并实施一个闭环认知评估系统，以了解和监测儿童的长期进展，并为认知专家提供建议和决策支持，以便他们做出更好的治疗决策。

The identification of cognitive impairments in early childhood provides the best opportunity for successful remedial intervention, because brain plasticity diminishes with age. Attention deficit hyperactivity disorder (ADHD) is a psychiatric neurodevelopmental disorder that is very hard to diagnose or tell apart from other disorders. Symptoms include inattention, hyperactivity, or acting impulsively, all of which often result in poor performance in school and persist later in life. In this project, an interdisciplinary team of computer and neurocognitive scientists will develop and implement transformative computational approaches to evaluate the cognitive profiles of young children and to address these issues. The project will take advantage of both physical and computer based exercises already in place in 300 schools in the United States and involving thousands of children, many of whom have been diagnosed with ADHD or other learning disabilities. Project outcomes will have important implications for a child's success in school, self-image, and future employment and community functioning. The PIs will discover new knowledge about the role of physical exercise in cognitive training, including correlations between individual metrics and degree of improvement over time. They will identify important new metrics and correlations currently unknown to cognitive scientists, which will have broad impact on other application domains as well. And the PIs will develop an interdisciplinary course on computational cognitive science and one on user interfaces for neurocognitive experts.The research will involve four thrusts. The PIs will devise new human motion analysis and computer vision algorithms that can automatically assess embodied cognition during structured physical activities, and which will constitute a breakthrough in improving the accuracy and efficiency of cognitive assessments of young children. Intelligent mining techniques will be used to discover new knowledge about the role of physical exercise in cognitive training and to find correlations between individual metrics and degree of improvement over time. A methodology will be developed using advanced multimodal sensing to collect and process huge amounts of evidence based assessment data with intelligent mechanisms that learn about a child's executive function capabilities and help uncover possible causes of cognitive dysfunctions. And a closed loop cognitive assessment system will be designed and implemented to understand and monitor a child's progress over time and provide recommendations and decision support to cognitive experts so they can make better treatment decisions.