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

该项目旨在开发创新技术，帮助治疗师在与患有焦虑或抑郁症的青少年进行谈话治疗期间评估和康复社交恐惧。根据世界卫生组织的数据，抑郁和焦虑是全球残疾的主要原因，影响约25%的人口，每年给全球经济造成1万亿美元的损失。治疗青少年焦虑或抑郁的标准方法包括每周与治疗师进行多次一对一的暴露疗法。暴露疗法逐渐将青少年暴露在引发他们恐惧的现实社会环境中，同时为他们提供管理和忍受痛苦的工具。高达50%的青少年对这种治疗方法没有反应，使他们处于慢性症状，自杀，残疾和预期寿命显着缩短的高风险中。治疗失败的部分原因是很难在治疗过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面对他们的恐惧。目前还没有专门设计用于在暴露治疗期间评估、重现和恢复社交恐惧的市售产品。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，该技术可以创建模拟现实生活中的情景，在治疗环境中引发社交恐惧，以及社交恐惧的标记，这些标记将用于为青少年及其治疗师提供客观和可操作的反馈，因为他们在治疗过程中练习技术。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提供多学科培训机会，以研究生水平为重点，包括来自边缘化社区的学员。该项目的研究目标是为社交恐惧的青少年介绍一种基于AR引导的脑电图（EEG）暴露技术的临床应用原型。拟议的技术将：（i）使用新的硬件集成和软件开发，在现实生活中的人际关系情境中，将EEG数据采集与社交恐惧场景的AR呈现无缝同步;（ii）通过EEG特征选择和贝叶斯推理方法，准确且连续地检测青少年是否表现出恐惧与不恐惧的反应（iii）设计新颖的机器学习方法来识别基于EEG的个体化恐惧指数;（iv）在真实的时间内监测基于EEG的恐惧指数，并在必要时调整AR社交恐惧场景以增加恐惧水平;以及（v）识别个体化阈值以检测用户何时处于“暴露区”中并提供视觉反馈以提示青少年何时应用治疗师规定的特定暴露技术。所提出的系统有可能提供一个技术驱动的范式暴露疗法，有意义地反映了抑郁和焦虑的青少年所经历的社会挑战。贝叶斯最优统计推断将支持该技术，提供神经驱动的框架，提高EEG记录与基于AR的头部跟踪的准确性。成果将在同行评审的文章、推广计划和代码/数据库中公开传播。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.