CAREER: Towards a Biologically Informed Intervention for Emotionally Dysregulated Adolescents and Adults with Autism Spectrum Disorder

职业：对患有自闭症谱系障碍的情绪失调青少年和成人进行生物学干预

• 批准号: 1844885
• 负责人: Murat Akcakaya
• 金额: $ 55万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844885&HistoricalAwards=false
• 关键词: CAREER; Towards; Biologically; Informed; Intervention

The investigator plans to improve systems that rely on brain-computer interfaces that are used in virtual and augmented reality environments. These improvements will enhance comfort and reliability when used by individuals with disorders within the autism spectrum. The benefits of these improvements will advance the effectiveness of treatments for emotion regulation and behavioral interventions. There is a growing interest in complementing such behavioral clinical treatments with various low-cost and easy-to-access technology-based tools to improve therapy efficacy. However, it was shown that training through existing technology-based Autism Spectrum Disorder (ASD) intervention tools does not usually generalize to real-life activities for many reasons. The investigator will develop an intervention for ASD to reinforce emotion regulation strategies based on real-time monitoring and analysis. Specifically, the planned electroencephalography (EEG)-guided brain-computer interface (BCI) technology could be used to complement all clinical treatments that focus on emotion regulation to decrease clinician time spent with each patient. The novel scientific discoveries and engineering enhancements will have overreaching contributions to develop ASD intervention techniques for (i) decreased depression and anxiety; (ii) decreased problematic behaviors including aggression in social interactions; and (iii) decreased functional impairment across different settings including school, work, home and community. Research and education goals will include: (i) course development; (ii) inclusion of researchers from K-12 to graduate level in cutting-edge interdisciplinary research environment to promote STEM careers; and (iii) establishing new outreach activities to inform the broader public about the proposed research outcomes and the latest technological advancements in research for technology-based ASD intervention. The research objective of this specific project is to introduce a framework that will enable EEG-guided closed-loop: (i) monitoring of the brain responses of individuals during technology-based ASD intervention, and (ii) control of the presentation of clinical treatment strategy cues for emotion regulation in individuals with ASD. In particular, for such human-computer interfaces: (1) the proposition of monitoring brain responses through EEG during ASD intervention is novel, and (2) formulating design principles for model-based optimal EEG-guided closed-loop clinical treatment strategy cue presentation is transformative. These propositions of real-time probabilistic analysis of EEG are unique, and present a potentially game-changing opportunity to advance the generalization effect of existing technology-based ASD intervention for emotion regulation. This project will also contribute to developing new machine learning algorithms and neuroscience methods to identify EEG features associated with emotion regulation to classify between distress and non-distress conditions, and to distinguish among different distress levels. The developed models will be based on a solid mathematical framework based on variational autoencoders and Bayesian optimal statistical inference, information theoretic measures of feature selection for efficient learning, and computationally efficient optimization of modular and submodular monotonic or non-monotonic functions. Optimization algorithms will provide computationally efficient solutions that generate(sub)optimal feature selection strategies with performance guarantees.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.

研究人员计划改进依赖于虚拟和增强现实环境中使用的脑机接口的系统。这些改进将提高自闭症谱系障碍患者使用时的舒适度和可靠性。这些改进的好处将提高情绪调节和行为干预治疗的有效性。 人们越来越有兴趣用各种低成本且易于访问的基于技术的工具来补充此类行为临床治疗，以提高治疗效果。然而，研究表明，由于多种原因，通过现有基于技术的自闭症谱系障碍 (ASD) 干预工具进行的培训通常无法推广到现实生活中的活动。研究人员将开发一种针对自闭症谱系障碍的干预措施，以基于实时监测和分析来加强情绪调节策略。具体来说，计划中的脑电图（EEG）引导的脑机接口（BCI）技术可用于补充所有专注于情绪调节的临床治疗，以减少临床医生在每位患者身上花费的时间。新的科学发现和工程改进将对开发自闭症谱系障碍 (ASD) 干预技术做出巨大贡献，以：(i) 减少抑郁和焦虑； (ii) 减少问题行为，包括社交互动中的攻击行为； (iii) 减少不同环境下的功能障碍，包括学校、工作、家庭和社区。研究和教育目标将包括：(i) 课程开发； (ii) 将从 K-12 到研究生水平的研究人员纳入尖端跨学科研究环境，以促进 STEM 职业发展； (iii) 开展新的外展活动，让更广泛的公众了解拟议的研究成果以及基于技术的 ASD 干预研究的最新技术进展。 该特定项目的研究目标是引入一个框架，该框架将实现脑电图引导的闭环：(i) 在基于技术的 ASD 干预期间监测个体的大脑反应，以及 (ii) 控制临床治疗的呈现自闭症谱系障碍患者情绪调节的策略线索。特别是，对于此类人机界面：（1）在 ASD 干预期间通过脑电图监测大脑反应的主张是新颖的，以及（2）为基于模型的最佳脑电图引导闭环临床治疗策略线索呈现制定设计原则是变革性的。 这些脑电图实时概率分析的主张是独一无二的，并提供了一个潜在的改变游戏规则的机会，以提高现有基于技术的 ASD 情绪调节干预的泛化效果。 该项目还将有助于开发新的机器学习算法和神经科学方法，以识别与情绪调节相关的脑电图特征，以对痛苦和非痛苦条件进行分类，并区分不同的痛苦程度。 开发的模型将基于一个坚实的数学框架，该框架基于变分自动编码器和贝叶斯最优统计推断、用于高效学习的特征选择的信息论测量，以及模块化和子模块化单调或非单调函数的计算有效优化。 优化算法将提供计算高效的解决方案，生成具有性能保证的（次）最优特征选择策略。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

EEG-based texture roughness classification in active tactile exploration with invariant representation learning networks

使用不变表示学习网络进行主动触觉探索中基于脑电图的纹理粗糙度分类

• DOI: 10.1016/j.bspc.2021.102507
• 发表时间: 2021-05
• 期刊: Biomedical Signal Processing and Control
• 影响因子: 5.1
• 作者: Özdenizci, Ozan;Eldeeb, Safaa;Demir, Andaç;Erdoğmuş, Deniz;Akçakaya, Murat
• 通讯作者: Akçakaya, Murat

Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometry

递归贝叶斯分类的停止准则设计：分析和决策几何

• DOI: 10.1109/tpami.2021.3075915
• 发表时间: 2021-01
• 期刊: IEEE Transactions on Pattern Analysis and Machine Intelligence
• 影响因子: 23.6
• 作者: Kocanaogullari, Aziz;Akcakaya, Murat;Erdogmus, D.
• 通讯作者: Erdogmus, D.

Quantitative EEG Changes in Youth With ASD Following Brief Mindfulness Meditation Exercise

患有自闭症谱系障碍的青少年在短暂的正念冥想练习后脑电图的定量变化

• DOI: 10.1109/tnsre.2022.3199151
• 发表时间: 2022
• 期刊: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
• 作者: Busra T. Susam;N. Riek;Kelly B. Beck;Safaa M. Eldeeb;C. Hudac;P. Gable;Caitlin M. Conner;M. Akçakaya;S. White;C. Mazefsky
• 通讯作者: C. Mazefsky

Geometric Analysis of Uncertainty Sampling for Dense Neural Network Layer

密集神经网络层不确定性采样的几何分析

• DOI: 10.1109/lsp.2021.3072292
• 发表时间: 2021-01
• 期刊: IEEE Signal Processing Letters
• 影响因子: 3.9
• 作者: Kocanaogullari, Aziz;Smedemark;Akcakaya, Murat;Erdogmus, Deniz
• 通讯作者: Erdogmus, Deniz

An Event-Driven AR-Process Model for EEG-Based BCIs With Rapid Trial Sequences

具有快速试验序列的基于 EEG 的 BCI 的事件驱动 AR 过程模型

• DOI: 10.1109/tnsre.2019.2903840
• 发表时间: 2019-05
• 期刊: IEEE Transactions on Neural Systems and Rehabilitation Engineering
• 影响因子: 4.9
• 作者: Gonzalez;Marghi, Yeganeh M.;Azari, Bahar;Akcakaya, Murat;Erdogmus, Deniz
• 通讯作者: Erdogmus, Deniz