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）：认为面部情绪识别的困难（FER）被认为会导致或加剧自闭症谱系障碍患者（ASD）的社会残疾1，通过防止1）准确检测到通过脸部传达的社交/情感信息，尤其是眼部的社交/情感信息，尤其是眼睛区域，以及2）在情感上适当的反应部署。尽管被认为是ASD中缺陷的神经系统受到越来越多的欣赏，但它们的可塑性仍然是投机性的。该项目的目的是开发一种辅助技术，以促进ASD中的面部情感识别[R21]。我们建议可以使用脑部计算机界面（BCI）设备来修复FER [R33]。要开发FER助手，我们计划首先[R21]确定是否可以开发多素分类器，该分类器在功能磁共振成像（fMRI）期间具有时间预测成功的情绪识别。自适应的实时fMRI（RT-FMRI）范式将解释受试者大脑的输出，以评估是否认识到计算机生成的演员的情绪。如果没有，则表达的面部情感将增加强度，直到计算机确定受试者已经认识到情绪为止。在调整了由支持矢量机（SVM）产生的这种监督学习算法之后，我们然后将大量多维分类器转换为低维空间，可以通过放置在头皮上的单个或双EEG传感器来复制该算法。原理的证明是多元分类器可以转发为频率（EEG）空间。 EEG传感器可以在扫描仪（BCI设备）外舒适地佩戴，并且可以无线连接到便携式平板电脑（iPad）。然后，我们将证明在组间，随机设计（真正的神经反馈与安慰剂神经反馈）中，卧床BCI'FER助手'[R33]的可行性。 FER Assistant是一种基于虚拟现实的iPad应用程序，它使用脑电图传感器数据来通过操纵阿凡达的情感强度来帮助用户识别情感，直到被用户识别出来为止，他们将获得点越早识别情绪。这项随机对照试验（RCT）的目的是评估可行性，包括干预，招聘和随机程序的可接受性，招聘和随机性程序，干预实施，盲目的评估程序以及在RCT的背景下保留，以准备一项功能齐全的功效试验。这项研究的产品包括表明缺陷和可塑性证据的神经过程，以及易于出口的基于计算机的干预措施。对于这一研究不足的人群，几乎没有治疗研究，社会缺陷可能会在青春期和成年初期对ASD患者面临独特的挑战，因为他们面临着需要社会能力的多个生活过渡和发展任务（例如，确保就业）。最终，我们计划评估这种紧急干预措施在足够动力的随机临床试验中的功效。