Collaborative Research: Development of a precision closed loop BCI for socially fearful teens with depression and anxiety

合作研究：为患有抑郁症和焦虑症的社交恐惧青少年开发精确闭环脑机接口

• 批准号: 2327065
• 负责人: Mary Woody
• 金额: $ 35万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2026-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327065&HistoricalAwards=false
• 关键词: Collaborative; Research; Development; precision; closed

This project aims to develop innovative technology that can assist therapists in assessing and rehabilitating social fears during talk therapy with teens suffering from anxiety or depression. According to the World Health Organization, depression and anxiety are leading causes of disability worldwide, affecting approximately 25% of the population and costing the global economy $1 trillion per year. The standard approach to treating teen anxiety or depression involves many weekly sessions of one-on-one exposure therapy with a therapist. Exposure therapy gradually exposes teens to real-life social situations that trigger their fears while providing them with tools to manage and tolerate their distress. Up to 50% of teens do not respond to this treatment approach, placing them at high risk for chronic symptoms, suicidality, disability, and a significantly shorter life expectancy. Treatment failure occurs partially because it is hard to recreate real-life social challenges within therapy sessions, so teens are not able to practice facing their fears under the direct supervision of their therapists. There is currently no commercially available product specifically designed to assess, recreate, and rehabilitate social fears during exposure therapy. This project, led by a multidisciplinary team, aims to address these challenges by developing augmented reality (AR) technology that creates simulated real-life situations that evoke social fears within the therapy environment, and markers of social fear that will be used to provide objective and actionable feedback to teens and their therapists as they practice techniques during therapy sessions. To achieve these goals, the project will include partnerships with community experts and offer multidisciplinary training opportunities for K-12 to graduate level, with an emphasis on inclusion of trainees from marginalized communities.The research objective of this project is to introduce a prototype for clinical application of an AR-guided, electroencephalogram (EEG)-based exposure technology for socially fearful teens. The proposed technology will: (i) use novel hardware integration and software development to seamlessly synchronize EEG data acquisition with AR presentation of social fear scenarios during real-life interpersonal situations; (ii) accurately and continuously detect whether the teen is exhibiting fearful vs. not fearful responses through EEG feature selection and Bayesian inference methods (considering both individualized responses and generalized responses across population); (iii) design novel machine learning methods to identify individualized EEG-based fear indices; (iv) monitor EEG-based fear indices in real time and adjust the AR social fear scenarios to increase the level of fear when necessary; and (v) identify individualized thresholds to detect when the user is in the “exposure zone” and provide visual feedback to prompt the teen when to apply specific exposure techniques as prescribed by the therapist. The proposed system has the potential to provide a technology-driven paradigm for exposure therapy that meaningfully reflects the social challenges experienced by depressed and anxious teens. Bayesian optimal statistical inference will support the technology, providing mathematically-driven frameworks that enhance the accuracy of EEG recordings coupled with AR-based headtracking. Outcomes will be openly disseminated in peer-reviewed articles, outreach programs, and code/data repositories.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在开发创新的技术，以帮助治疗师在与焦虑或抑郁症的青少年聊天期间评估和修复社会恐惧。根据世界卫生组织的数据，抑郁症和焦虑是全球残疾的主要原因，影响了大约25％的人口，每年损失全球经济1万亿美元。治疗青少年动画或抑郁症的标准方法涉及与治疗师一对一接触疗法的每周一次。暴露疗法逐渐使青少年暴露于现实生活中的社会情况，这些情况引发了他们的恐惧，同时为他们提供了管理和容忍痛苦的工具。多达50％的青少年对这种治疗方法没有反应，使他们面临慢性症状，自杀，残疾和预期寿命的高风险。治疗失败之所以发生部分原因是因为很难在治疗课程中重现现实生活中的社会挑战，因此青少年无法在治疗师的直接监督下练习恐惧。目前尚无专门设计的商业产品，用于评估，重现和康复在暴露疗法期间的社会恐惧。该项目由一个多学科团队领导，旨在通过开发增强现实（AR）技术来应对这些挑战，从而创建模拟现实生活中的情况，从而引起现实生活中的恐惧，并在治疗环境中引起社会恐惧的标志，这些恐惧的标志将用于在治疗期间为青少年及其治疗师提供客观和可行的反馈。为了实现这些目标，该项目将包括与社区专家的合作伙伴关系，并为K-12提供多学科的培训机会，重点是包括边缘化社区的受训者。该项目的研究目标是为AR指导的，电脑电图（EEG）基于基于的曝光技术的临床应用介绍一个原型。拟议的技术将：（i）使用新颖的硬件集成和软件开发来无缝地同步脑电图数据获取，并在现实生活中的人际关系中呈现社会恐惧情景； （ii）准确且连续地检测到这名青少年是否通过脑电图选择和贝叶斯推论方法表现出可怕的反应，而不是恐惧的反应（考虑跨人群的个性化反应和普遍反应）； （iii）设计新颖的机器学习方法，以识别基于脑电图的个性化恐惧指数； （iv）实时监控基于脑电图的恐惧指标，并调整社会恐惧情景，以提高必要时的恐惧程度； （v）确定个性化阈值，以检测用户何时在“曝光区”中，并提供视觉反馈，以提示青少年在应用治疗师规定的特定曝光技术时。拟议的系统有可能为暴露疗法提供技术驱动的范式，从而有意义地反映了沮丧和焦虑的青少年所经历的社会挑战。贝叶斯最佳统计推断将支持该技术，提供数学驱动的框架，从而提高了EEG录音的准确性以及基于AR的插图。结果将在经过同行评审的文章，外展计划和代码/数据存储库中公开传播。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估审查标准，被认为是通过评估来获得的支持。