SCH: EXP: Collaborative Research: THink - Inferring Cognitive State From Subtle Behaviors

SCH：EXP：协作研究：思考 - 从微妙行为推断认知状态

• 批准号: 1404494
• 负责人: Randall Davis
• 金额: $ 47.5万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1404494&HistoricalAwards=false
• 关键词: SCH; EXP; Collaborative; Research; THink

Cognitive disorders are a key health problem affecting millions of individuals, a problem that is increasing in importance as America's population ages. Just a single dementia, Alzheimer's disease, is estimated to afflict some 5.4 million Americans in 2012, projected to rise to 11-16 million by 2050, with the potential for staggering cost both in dollars and human costs for the afflicted and their caregivers. Yet cognitive health is not monitored effectively in the way that physical conditions and wellness are: physician visits routinely include screens for heart disease, diabetes, etc., but there is far less consistent screening for cognitive status. This project seeks to develop rapid, precise, and objective cognitive assessment tools that will provide operationally defined, quantitative metrics with sufficient sensitivity and specificity to aid in early detection and diagnosis of neurodegenerative disorders, as well as monitoring disease process and treatment efficacy. Its evidence-based approach offers the possibility of earlier treatment, which can slow the progress of cognitive disease, resulting in better quality of life.The first research objective is to develop two new digital versions of two traditional tests. The digital Maze Completion (dMC) assesses frontal brain system associated cognitive functions by providing measures of tiered decision making and executive planning, and calibration of memory function by measuring the effect of priming. The digital Symbol-Digit Test (dSDT) assesses executive function by measuring information processing speed, and calibrates planning and cognitive shifting by detecting and measuring subtle behaviors. Both tests provide new measures of learning, assessing reaction time changes and short term memory retention. The second objective is to develop the algorithms required to detect subtle behaviors during the dMC and dSDT tests, which can serve as a platform to which additional tests can easily be added. The third objective is to make all the tests as self-scoring as possible, through advances in signal interpretation. The fourth objective involves administering the dCDT, dMC and dSDT to both healthy adults and subjects with known cognitive impairments, permitting the calibration of the power and value of the tests. The final objective is to establish the validity of selected measures provided by the tests, by comparing the measures to standard cognitive test results, to specific neuroanatomical structures, and to external and subcortical gray matter integrity relative to white matter integrity. These research activities are integrated with education through projects in courses, engagement of graduate and undergraduate students in the research, and annual workshops for students.

认知障碍是影响数百万人的关键健康问题，随着美国人口老龄化，这一问题变得越来越重要。据估计，2012 年，仅一种痴呆症——阿尔茨海默病就将影响约 540 万美国人，预计到 2050 年这一数字将增至 11-1600 万，这可能给患者及其护理人员带来惊人的美元和人力成本。然而，认知健康并没有像身体状况和健康那样得到有效监测：医生就诊通常包括对心脏病、糖尿病等的筛查，但对认知状态的筛查却远没有那么一致。该项目旨在开发快速、精确和客观的认知评估工具，提供可操作定义的、具有足够敏感性和特异性的定量指标，以帮助早期发现和诊断神经退行性疾病，以及监测疾病过程和治疗效果。其基于证据的方法提供了早期治疗的可能性，这可以减缓认知疾病的进展，从而提高生活质量。第一个研究目标是开发两种传统测试的两种新的数字版本。 数字迷宫完成 (dMC) 通过提供分层决策和执行计划的测量来评估额叶大脑系统相关的认知功能，并通过测量启动效果来校准记忆功能。数字符号数字测试 (dSDT) 通过测量信息处理速度来评估执行功能，并通过检测和测量微妙行为来校准计划和认知转变。这两项测试都提供了新的学习衡量标准，评估反应时间变化和短期记忆保留。第二个目标是开发在 dMC 和 dSDT 测试期间检测细微行为所需的算法，该算法可以作为可以轻松添加其他测试的平台。第三个目标是通过信号解释的进步，使所有测试尽可能自我评分。第四个目标涉及对健康成人和已知认知障碍的受试者进行 dCDT、dMC 和 dSDT，从而校准测试的功效和价值。最终目标是通过将测量值与标准认知测试结果、特定神经解剖结构以及相对于白质完整性的外部和皮质下灰质完整性进行比较，确定测试提供的选定测量值的有效性。 这些研究活动通过课程项目、研究生和本科生参与研究以及为学生举办的年度研讨会与教育相结合。