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: 9131476
• 负责人: Susan Williams White
• 金额: $ 34.61万
• 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 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9131476
• 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; Health; Individual; Intervention; Investigation; Knowledge; Learning; Life; Link; Machine Learning; Mental Health; Modeling; Morbidity - disease rate; Nature; Neuronal Plasticity; Outcome; Output; Participant; Pattern; Placebos; Population; 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; relating to nervous system; response; satisfaction; sensor; skills; social; social skills; study population; tool; 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)的困难被认为是导致或加剧自闭症谱系障碍(ASD)患者的社交残疾的原因，原因是：1)准确检测通过面部，特别是眼睛区域传达的社交/情绪信息，以及2)部署情绪上适当的反应。尽管被认为是ASD中FER缺陷的基础的神经系统越来越受到重视，但它们的可塑性仍然是推测的。该项目的目标是开发一种辅助技术，以促进ASD的面部情绪识别[R21]。我们建议可以使用脑机接口(BCI)设备恢复FER[R33]。为了开发FER助手，我们计划首先确定是否有可能开发一种在功能磁共振成像(FMRI)期间能够在时间上预测成功情绪识别的多体素分类器。自适应实时功能磁共振成像(RT-fMRI)范式将解释受试者大脑的输出，以评估计算机生成的演员的情感是否被识别。如果没有，表达的面部情绪将增加强度，直到计算机确定受试者已经识别了这种情绪。在对支持向量机产生的监督学习算法进行调整后，我们将大规模多维分类器转换到低维空间，这可以通过放置在头皮上的单或双脑电传感器来复制。原理证明，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)之外，并可以无线连接到便携式平板电脑(IPad)上。然后，我们将在组间随机设计(真正的神经反馈与安慰剂神经反馈)中证明动态BCI‘FER助手’[R33]的可行性。FER助手是一个基于虚拟现实的iPad应用程序，它使用EEG传感器数据来帮助用户进行情感识别，方法是操纵化身的情感强度，直到用户识别到为止，用户将在识别情感的越早获得分数。这项随机对照试验(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、盲法评估程序以及在RCT的背景下留住受试者，为有效的疗效试验做准备。这项研究的成果包括FER缺陷背后的神经过程的演示及其可塑性的证据，以及一种易于输出的、成本最低的基于计算机的干预。对这一研究不足的人群的治疗研究很少，社会缺陷可能会给自闭症患者在青春期后期和成年期早期带来独特的挑战，因为他们面临着多次人生转型和需要社会能力的发展任务(例如，确保就业)。最终，我们计划在一项动力充足的随机临床试验中评估这种紧急干预的疗效。