Scalable Computational Platform For Active Closed-Loop Behavioral Coding in Autism Spectrum Disorder

用于自闭症谱系障碍主动闭环行为编码的可扩展计算平台

• 批准号: 10440249
• 负责人: Geraldine Dawson
• 金额: $ 38.67万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10440249
• 关键词: Address; Affect; Age; Algorithms; Attention; Attention deficit hyperactivity disorder; Award; Behavior; Behavior assessment; Behavior monitoring; Behavioral; Behavioral Sciences; Behavioral Symptoms; Big Data; Biological Markers; Case-Control Studies; Cephalometry; Child; Childhood; Clinical; Clinical Sciences; Code; Collection; Communities; Computer Vision Systems; Computing Methodologies; Data; Data Set; Detection; Development; Developmental Delay Disorders; Devices; Diagnosis; Diagnostic; Electroencephalography; Electronic Health Record; Engineering; Environment; Evaluation; Funding; Future; General Population; Goals; Gold; Hand; Home; Knowledge; Language Delays; Low income; Machine Learning; Matched Group; Measures; Methods; Molecular Genetics; Monitor; Motion; Motor; Movement; Neurodevelopmental Disorder; Neurosciences; Outcome; Participant; Pattern; Periodicity; Phenotype; Population; Positioning Attribute; Posture; Primary Health Care; Questionnaires; Research Personnel; Risk; Sampling; Schools; Site; Standardization; Stimulus; Symptoms; Tablets; Time; Toddler; Training; United States National Institutes of Health; Work; actigraphy; autism spectrum disorder; autistic children; base; behavioral phenotyping; computational platform; computer design; computer science; computerized tools; data integration; design; developmental disease; diagnosis standard; digital; gaze; improved; large datasets; machine learning algorithm; motor behavior; multimodal data; multimodality; novel; preference; recruit; screening; sensor; social; tool; visual tracking

SCALABLE COMPUTATIONAL PLATFORM FOR ACTIVE CLOSED-LOOP BEHAVIORAL CODING IN AUTISM SPECTRUM DISORDER ABSTRACT Despite significant recent advances in molecular genetics and neuroscience, behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in neurodevelopmental disorders, including autism spectrum disorder (ASD). Such behavioral ratings are subjective, require significant clinician expertise and training, typically do not capture data from the children in their natural environments, and are not scalable for large population screening, low-income communities, or longitudinal monitoring. The development of scalable digital approaches to standardized objective behavioral assessment is thus a significant unmet need in ASD, here addressed via machine learning and computer vision with the goal of providing scalable methods for assessing existing biomarkers, from eye tracking to movement and posture patterns, and tools for novel discovery. Our long-term goal is to develop validated scalable tools for the automatic behavioral analysis of neurodevelopmental disorders. The proposed computational project leverages results and big data derived from our previous studies (N=1,864 participants) and our recently funded NIH Autism Center of Excellence (ACE) award (N=7,436 participants). The ACE project will allow us to develop and validate our tools on several thousand toddlers recruited in Duke pediatric primary care and followed longitudinally for whom gold-standard diagnoses of ASD, attention deficit hyperactivity disorder (ADHD), developmental and language delay and extensive electronic health record (EHR) data will be available; and in a case control study of 224 age-matched groups of young children with ASD, ADHD, and typical development from whom gold-standard diagnostic, extensive phenotypic, Tobii eye- tracking, and EEG will be collected. This project aims to develop novel computational methods using these datasets, from sensing in scalable fashion behaviors such as attention and gaze (Aim 1) and motor/posture (Aim 2), to their multimodal integration (Aim 3). A unique aspect of our computational approach is the closed- loop integration of stimuli design for actively eliciting behavioral symptoms, use of consumer-grade sensors, and automatic behavioral analysis. This contrasts with the current approach of independently selecting stimuli and using expensive lab-based professional grade sensors with off-the-shelf algorithms to capture behavioral biomarkers expected from the stimuli. Our approach involves active elicitation of behavior which is also different from commonly used digital approaches that involve gathering large datasets from passive sensing, such as actigraphy monitoring of spontaneous behavior at home. Our framework results in active closed-loop sensing, where participants are engaged in short and developmentally appropriate activities on ubiquitous devices, while the sensors in the same device capture information for the automatic and quantitative analysis of behavioral biomarkers. This scalable, objective, and standardized way of stimulating, sensing, and analyzing allows the collection of large behavioral datasets for machine learning.

一种可扩展的主动闭环行为编码计算平台 自闭症谱系障碍 摘要 尽管分子遗传学和神经科学最近取得了重大进展，但基于 临床观察仍然是筛查、诊断和评估预后的金标准。 神经发育障碍，包括自闭症谱系障碍(ASD)。这样的行为评级是 主观性，需要大量的临床医生专业知识和培训，通常不会捕获儿童的数据 他们的自然环境，而且不能扩展到大规模人口筛查、低收入社区或 纵向监测。开发可扩展的数字方法以实现目标行为标准化 因此，评估在ASD中是一个重要的未得到满足的需求，这里通过机器学习和计算机来解决 视觉，目标是提供可扩展的方法来评估现有的生物标记物，从眼睛跟踪到 运动和姿势模式，以及新发现的工具。我们的长期目标是开发经过验证的 用于神经发育障碍的自动行为分析的可扩展工具。建议数 计算项目利用了我们之前研究的结果和大数据(N=1,864名参与者) 以及我们最近资助的NIH自闭症卓越中心(ACE)奖(N=7,436名参与者)。《王牌》 该项目将允许我们在杜克儿科招募的数千名幼儿身上开发和验证我们的工具 初级保健和纵向跟踪诊断ASD、注意力缺陷的黄金标准 多动障碍(ADHD)、发育和语言延迟以及大量的电子健康记录 (EHR)数据将可用；在对224名年龄匹配的儿童进行的病例对照研究中 ASD，ADHD，和典型的发育来自谁的黄金标准诊断，广泛的表型，Tobii眼- 跟踪，并收集脑电。这个项目的目的是开发新的计算方法，使用这些 数据集，来自以可扩展方式感知的行为，如注意力和凝视(目标1)和运动/姿势 (目标2)，促进多式联运一体化(目标3)。我们的计算方法的一个独特方面是封闭的- 刺激环路集成设计，用于主动引发行为症状，使用消费级传感器， 和自动行为分析。这与目前独立选择刺激的方法形成了鲜明对比 并使用昂贵的基于实验室的专业级传感器和现成的算法来捕获行为 从刺激中预期的生物标记物。我们的方法包括主动激发行为，这也是 与通常使用的涉及从被动感测收集大数据集的数字方法不同， 例如，在家中对自发行为进行动态监测。我们的框架导致了主动闭环系统 感知，参与者在无处不在的 设备，而同一设备中的传感器捕获信息，用于自动和定量分析 行为生物标记物。这种可扩展、客观和标准化的刺激、感知和分析方式 允许收集用于机器学习的大型行为数据集。