A machine learning computational approach for developing synchronized EEG and behavior biomarkers in young autistic children

用于开发自闭症儿童同步脑电图和行为生物标志物的机器学习计算方法

• 批准号: 10523409
• 负责人: Kimberly L H Carpenter
• 金额: $ 8.62万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-07 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10523409
• 关键词: 6 year old; Address; Age; Attention; Behavior; Biological; Biological Markers; Brain; Caregivers; Child; Child Behavior; Clinical; Clinical Trials; Code; Computer Vision Systems; Computing Methodologies; Development; Devices; Discrimination; Early Intervention; Early identification; Electroencephalography; Event-Related Potentials; Exhibits; Face; Facial Expression; Factor Analysis; Female; Goals; Heterogeneity; Individual; Intellectual functioning disability; Machine Learning; Measurement; Measures; Methods; Monitor; Nursery Schools; Outcome; Outcome Measure; Participant; Pathway Analysis; Performance; Phase; Phenotype; Quality of life; Reporting; Research; Rest; Sampling; Screening procedure; Sex Differences; Social Functioning; Source; Standardization; Stimulus; Stratification; Subgroup; Tablets; Technology; Testing; Variant; Visual evoked cortical potential; autism spectrum disorder; autistic; autistic children; base; behavioral outcome; behavioral response; biomarker discovery; biomarker performance; brain based; data acquisition; design; digital; digital health; gaze; improved; improved outcome; indexing; innovation; machine learning method; multimodality; neural; neurophysiology; novel; response; screening; sex; social; social attention; tool; visual tracking

ABSTRACT – Project 3 The overall goal of the Duke Autism Center of Excellence is to use a translational digital health and computational approach to address the critical need for more effective autism screening tools, objective outcome measures, and brain-based biomarkers that can be used in clinical trials with young autistic children. Despite significant advances in understanding the biological basis of autism, clinical trials continue to rely on subjective clinical observation and caregiver report measures. Objective, biologically based biomarkers are needed for use in clinical trials that can parse heterogeneity, assess target engagement, and monitor outcomes. Autism biomarker studies have utilized electroencephalography (EEG) and eye-tracking measures, which have found differences between autistic and neurotypical individuals in neural and attentional processing of social stimuli. However, to date, the majority of autism biomarker studies have used independent experimental paradigms and separate analyses of EEG and gaze. Technical and computational advances, including machine learning and computer vision analysis, now allow for synchronized measurement and analysis of EEG and behavior, including eye- tracking, each of which provides distinct sources of information that can be integrated to improve biomarker performance. Project 3 will use an innovative machine learning computational method to develop a multimodal biomarker that combines features of EEG activity and synchronized measures of children’s behavior (e.g., social attention) automatically coded via computer vision analysis. We will test the hypothesis that a multimodal biomarker will show enhanced discrimination between autistic and neurotypical children compared to biomarkers based on EEG alone. Standard and novel methods will be used to combine synchronized behavior (digital phenotypes) and EEG features, with a focus on neural connectivity measured via traditional methods (coherence, phase-lag index) and new network analysis methods (discriminative cross-spectral factor analysis) developed by our team. This multimodal approach will be evaluated in 3–6-year-old autistic children without intellectual disability (ID), age- and sex-matched neurotypical children, and autistic children with ID (IQ <= 70). Multimodal biomarkers will be compared to three commonly used EEG biomarkers. Our goal is to develop robust, brain-based biomarkers that can be used in clinical trials to evaluate early interventions for young autistic children designed to improve outcomes and quality of life.

摘要-项目3 杜克自闭症卓越中心的总体目标是使用一个转化的数字健康和计算 方法，以解决更有效的自闭症筛查工具，客观的结果措施， 以及可用于自闭症儿童临床试验的大脑生物标志物。尽管取得了重大 随着对自闭症生物学基础的理解的进步，临床试验继续依赖于主观的临床试验。 观察和看护者报告措施。目的，需要基于生物学的生物标志物用于 临床试验，可以解析异质性，评估目标参与，并监测结果。自闭症生物标志物 研究利用脑电图（EEG）和眼动跟踪测量，发现差异 自闭症和神经型个体在社会刺激的神经和注意力处理方面的差异。但要 迄今为止，大多数自闭症生物标志物研究都使用独立的实验范式， EEG和凝视的分析。技术和计算进步，包括机器学习和计算机 视觉分析，现在允许同步测量和分析EEG和行为，包括眼睛， 跟踪，其中每一个都提供了不同的信息源，可以整合这些信息源以改善生物标志物 性能项目3将使用一种创新的机器学习计算方法来开发多模态 结合EEG活动特征和儿童行为同步测量的生物标志物（例如，社会 注意）通过计算机视觉分析自动编码。我们将检验一个多模态 与生物标志物相比，生物标志物将显示自闭症儿童和神经典型儿童之间的区分增强 仅仅根据脑电图。标准和新颖的方法将用于联合收割机同步行为（数字 表型）和EEG特征，重点是通过传统方法测量的神经连接 （相干性、相位滞后指数）和新的网络分析方法（判别交叉谱因子分析） 由我们的团队开发。这种多模式方法将在3-6岁的自闭症儿童中进行评估， 智力残疾（ID）、年龄和性别匹配的神经正常儿童和患有ID（IQ <= 70）的自闭症儿童。 将多模式生物标志物与三种常用的EEG生物标志物进行比较。我们的目标是开发强大的， 基于大脑的生物标志物，可用于临床试验，以评估自闭症儿童的早期干预措施 旨在改善结果和生活质量。