A Novel Approach to Assessing Cognitive State During Real-World Tasks

 描述（由申请人提供）：在第1阶段，我们的团队能够通过减少硬件系统的整体配置文件，引入实时评论标记真实的世界活动中的事件，并评估（和确认）当前伪影拒绝算法在真实的世界环境中的充分性，来增强ABM B-Alert系统在道路评估中的使用。此外，针对参与、分心/困倦和工作负荷的B-Alert算法经验证可用于HIV+人群，并应用于基于实验室的认知评估、模拟驾驶和道路驾驶。来自第1阶段的数据表明，传统的临床/实验室评估在预测现实世界的行为方面仅取得了一定的成功，可以通过添加EEG指标来丰富，并且这可能会大大改善对现实世界驾驶困难的预测。初步数据表明，道路驾驶的预测因素可能因艾滋病毒感染状况而异，年龄也可能是一个因素。 值得注意的是，艾滋病毒现在被认为是一种慢性、可控制的疾病，在有机会获得治疗的个人中。在美国，大约四分之一的HIV感染者（HIV+）年龄在50岁或以上，预计到2015年，该年龄组的患病率将达到50%。虽然艾滋病毒相关的神经认知障碍（HAND）不像前几代人那么严重，但它们仍然普遍存在，并可能影响日常功能，包括汽车驾驶。与其他条件一样，基于临床的NP评估只能适度预测真实的世界任务的成功或失败。鉴于这些因素，在第二阶段，我们建议强调EEG指标在老年HIV+个体中的应用。 该提案的目的是：1）提高模拟中B-Alert系统的易用性，以确保评估易于执行，只需最少的培训，2）开发更好地反映现实世界挑战的重点驾驶模拟（例如，车内任务），并有助于更强大的基于模拟器的EEG数据收集，和3）扩展目前的研究结果，以更大的样本集中在老年艾滋病毒参与者，谁将完成实验室模拟和道路上的评估。 该项目将在两个平行分支中进行。第一个分支将涉及改善我们使用B-Alert系统的便利性，扩展神经认知评估以获得更多驾驶特定技能（分散注意力、有用视野），并根据第1阶段经验修改模拟（设计筛选、评估和模拟）。为了优化易用性，数据质量监控选项（例如，将开发带有STI软件的窗口显示器中的窗口、问题的远程通知（如皮带蜂鸣器或远程屏幕上的弹出窗口），沿着带有干凝胶传感器接口的一步应用程序。第二个分支将通过关注老龄化人群并将样本量增加到50名HIV阳性和50名HIV-> 55岁的驾驶员来扩展我们的第1阶段结果。所有参与者将完成认知评估和模拟，每组一半将完成道路评估。作为该分支的一部分，我们还将利用半干电极的开发来评估这些传感器在实际应用中的数据质量实用性。参与者将从UCSD的HIV神经行为研究中心和Owen HIV诊所招募。 如果成功，该工具将1）为研究人员提供一种评估现实世界功能组成部分的新方法，2）验证第一个道路驾驶认知状态算法，3）开发道路驾驶障碍的预测器，减少对潜在危险的现实驾驶评估的需求，设想的最终产品将a）允许针对各种真实的世界应用的实验室内和野外评估的集成，B）可供研究人员、临床医生、和公共安全官员以及c）与广泛的条件相关，包括衰老和各种神经学（例如，中风，TBI恢复）和精神病（例如，物质使用）障碍。

 DESCRIPTION (provided by applicant): In Phase 1, our team was able to enhance the ABM B-Alert system for use in on the road evaluations by reducing the overall profile of the hardware system, introducing real-time commenting for marking events in real world activities, and assessing (and confirming) current artifact rejection algorithms for sufficiency in real world environments. In addition, the B-Alert algorithms for engagement, distraction/drowsiness, and workload were validated for use in the HIV+ population, and applied in laboratory-based cognitive assessments, simulated driving and on the road driving. The data from Phase 1 indicate that traditional clinic/laboratory assessments, which have been only modestly successful in predicting real-world behaviors, can be enriched by the addition of EEG metrics, and that this may substantially improve the prediction of real-world driving difficulties. Initial ata suggest that the predictors of on-road driving may vary by HIV status, and that age may be a factor as well. Of note, HIV is now considered a chronic, manageable condition in individuals who have access to treatment. Approximately one quarter of HIV infected (HIV+) persons in the U.S. are 50 years or older, with prevalence rates in this age group expected to reach 50% by 20153. While HIV-associated neurocognitive disorders (HAND) are not as severe as in previous generations1, they remain prevalent and can impact everyday functioning, including automobile driving. As with other conditions, clinic-based NP assessments only modestly predict success or failure at real world tasks. Given these factors, in Phase II we propose to emphasize the application of EEG metrics in older HIV+ individuals. The aims of this proposal are to: 1) improve the ease of use of the B-Alert system within the simulation to ensure that assessment is easily executed, with minimal training, 2) develop focused driving simulations that better reflect real-world challenges (e.g., in-car tasks) and contribute to more robust simulator-based EEG data collection, and 3) extend the current findings to a larger sample focused on aging HIV participants, who will complete both laboratory simulations and on-the-road evaluations. This project will occur in two parallel branches. The first branch will involve improving the ease of us of the B-Alert system and expanding the neurocognitive assessments for more driving specific skills (divided attention, useful field of view), and modifying the simulations based on Phase 1 experience (designing screening, and evaluation, simulations). To optimize ease of use, data quality monitoring options (e.g., window in window displays with STI software, remote notification of issues such as a belt buzzer, or pop up on a remote screen) will be developed, along with one-step applications with dry-gel sensor interface. The second branch will expand our Phase 1 results by focusing on an aging population and increasing the sample size to 50 HIV+ and 50 HIV- drivers > 55 years of age. All participants will complete the cognitive assessments and simulations, and half of each of group will complete the on-the-road assessments. As part of this branch, we will also leverage the development of semi-dry electrodes to evaluate the utility of these sensors for data quality in real-world applications. Participants will be recruited from the HIV Neurobehavioral Research Center and Owen HIV Clinic at UCSD. If successful, the proposed tool would 1) provide researchers with a new method for assessing components of real-world functioning, 2) validate the first on-road driving cognitive state algorithms, and 3) develop predictors of on-road driving impairments, reducing the need for potentially dangerous real-life driving assessments, The envisioned final product would a) allow for integration of in-laboratory and in-the-wild assessments for a variety of real world applications, b) be available for use by researchers, clinicians, and public safety officials and c) be relevant to a broad range of conditions, including aging and various neurologic (e.g., stroke, TBI recovery) and psychiatric (e.g., substance use) disorders.