Development of a novel neurotechnology to promote emotion recognition in autism

开发一种新型神经技术来促进自闭症患者的情绪识别

• 批准号: 8635153
• 负责人: Susan Williams White
• 金额: $ 26.97万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-03-12 至 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8635153
• 关键词: Adaptive Behaviors; Adolescence; Adolescent; Adult; Affect; Algorithms; Area; Autistic Disorder; Behavioral; Biological Markers; Blinded; Brain; Chronic; Clinical; Clinical Trials; Cognitive; Computer Simulation; Computers; Data; Data Analyses; Detection; Development; Devices; Electroencephalography; Emotional; Emotions; Employment; Eye; Face; Feedback; Frequencies; Functional Magnetic Resonance Imaging; Goals; Individual; Intervention; Investigation; Knowledge; Learning; Life; Link; Machine Learning; Mental Health; Modeling; Morbidity - disease rate; Nature; Neuronal Plasticity; Outcome; Output; Participant; Pattern; Placebos; Population; Population Study; Preparation; Procedures; Process; Randomized; Randomized Clinical Trials; Randomized Controlled Trials; Reporting; Research; Sampling; Scalp structure; Secure; Self-Help Devices; Signal Transduction; Social Environment; Symptoms; System; Tablets; Technology; Testing; Therapeutic; Time; Work; active method; autism spectrum disorder; base; brain computer interface; computer generated; control trial; cost; design; disability; efficacy trial; emerging adult; innovation; neurofeedback; neurotechnology; novel; prevent; programs; public health relevance; relating to nervous system; response; satisfaction; sensor; skills; social; social skills; tool; virtual; virtual reality

DESCRIPTION (provided by applicant): Difficulties in facial emotion recognition (FER) are thought to cause or exacerbate social disability in people with autism spectrum disorder (ASD) by preventing 1) accurate detection of social/emotional information conveyed through the face, particularly the eye-region, and 2) the deployment of emotionally appropriate responses. Although the neural systems thought to underlie FER deficits in ASD are increasingly appreciated, their plasticity remains speculative. The goal of this project is to develop an assistive technology to promote facial emotion recognition in ASD [R21]. We propose that FER can be rehabilitated using a brain-computer interface (BCI) device [R33]. To develop an FER assistant, we plan to first [R21] determine whether it is possible to develop a multi-voxel classifier that is temporally predictive of successful emotion recognition during functional magnetic resonance imaging (fMRI). An adaptive, real-time fMRI (rt-fMRI) paradigm will interpret the output of a subject's brain to assess whether a computer-generated actor's emotion is recognized. If not, the expressed facial emotion will be increased in intensity until the computer determines that the subject has recognized the emotion. After tuning this supervised learning algorithm produced by a support vector machine (SVM), we then transform the massively multidimensional classifier to low-dimensionality space, which can be replicated by a single- or dual-EEG sensor placed on the scalp. The proof of principle is that the multivariate classifier can be forward transformed into frequency (EEG) space. The EEG sensor can be comfortably worn outside of the scanner (BCI device), and can be wirelessly linked to a portable tablet (iPad). We will then demonstrate the feasibility of an ambulatory BCI 'FER assistant' [R33] in a between-group, randomized design (genuine neurofeedback vs placebo neurofeedback). The FER assistant is a virtual reality- based iPad application that uses the EEG sensor data to assist users with emotion recognition by manipulating the avatar's emotion intensity until it is recognized by the user, who will receive points the earlier the emotion is recognized. The purpose of this randomized controlled trial (RCT) is to assess feasibility including acceptability of the intervention, recruitment and randomization procedures, intervention implementation, blinded assessment procedures, and participant retention within the context of an RCT in preparation for a well- powered efficacy trial. This study's products include demonstration of the neural processes that underlie FER deficits and evidence of their plasticity, and an easily exportable, minimal-cost computer-based intervention. There has been little treatment research for this under-studied population, and social deficits may post unique challenges to people with ASD during late adolescence and early adulthood, as they face multiple life transitions and developmental tasks requiring social competence (e.g., securing employment). Ultimately, we plan to evaluate the efficacy of this emergent intervention in an adequately powered randomized clinical trial.

描述（由申请人提供）：面部情绪识别 (FER) 困难被认为会导致或加剧自闭症谱系障碍 (ASD) 患者的社交障碍，因为它会阻碍 1) 准确检测通过面部传达的社交/情绪信息，特别是面部表情。眼睛区域，以及 2) 部署情感上适当的反应。尽管人们越来越认识到自闭症谱系障碍中 FER 缺陷背后的神经系统，但它们的可塑性仍然是推测性的。该项目的目标是开发一种辅助技术来促进自闭症谱系障碍患者的面部情绪识别[R21]。我们建议可以使用脑机接口（BCI）设备来恢复 FER [R33]。为了开发 FER 助手，我们计划首先 [R21] 确定是否可以开发一种多体素分类器，该分类器可以在功能磁共振成像 (fMRI) 过程中暂时预测成功的情绪识别。自适应实时功能磁共振成像（rt-fMRI）范例将解释受试者大脑的输出，以评估计算机生成的演员的情绪是否被识别。如果没有，所表达的面部情绪将会增加强度，直到计算机确定受试者已经识别出该情绪。在调整支持向量机（SVM）产生的监督学习算法后，我们将大规模多维分类器转换为低维空间，可以通过放置在头皮上的单或双脑电图传感器来复制。原理证明是多元分类器可以前向变换到频率（EEG）空间。 EEG 传感器可以舒适地佩戴在扫描仪（BCI 设备）外部，并且可以无线连接到便携式平板电脑（iPad）。然后，我们将证明动态 BCI“FER 助手”[R33] 在组间随机设计（真实神经反馈与安慰剂神经反馈）中的可行性。 FER助手是一款基于虚拟现实的iPad应用程序，它使用脑电图传感器数据来帮助用户进行情绪识别，通过操纵化身的情绪强度直到被用户识别，越早识别出情绪的人将获得积分。本随机对照试验 (RCT) 的目的是评估可行性，包括干预的可接受性、招募和随机化程序、干预实施、盲法评估程序以及 RCT 背景下的参与者保留情况，为强有力的疗效试验做准备。这项研究的产品包括 FER 缺陷背后的神经过程的演示及其可塑性的证据，以及易于导出、成本最低的基于计算机的干预措施。针对这一研究不足的人群的治疗研究很少，而且社交缺陷可能会给青春期后期和成年早期的自闭症患者带来独特的挑战，因为他们面临着多重人生转变和需要社交能力的发展任务（例如，确保就业）。最终，我们计划在一项充分有力的随机临床试验中评估这种紧急干预措施的功效。