Investigating quantitative signatures of autism in toddlers

研究幼儿自闭症的定量特征

• 批准号: 10349536
• 负责人: Kristina Denisova
• 金额: $ 38.73万
• 依托单位: QUEENS COLLEGE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10349536
• 关键词: 3 year old; Adolescent; Adult; Age; Behavioral; Birth; Brain; Brain imaging; Characteristics; Child; Classification; Clinical; Cognitive; Communities; Complex; Computer Models; Data; Databases; Development; Diagnosis; Disease; Equilibrium; Esthesia; Functional Magnetic Resonance Imaging; Funding; Future; General Population; Goals; Head Movements; Hearing; Human; Impairment; Individual; Infant; Knowledge; Language; Learning; Life; Link; MRI Scans; Machine Learning; Methods; Motor; Motor Activity; Movement; National Institute of Mental Health; Neurobiology; Neurodevelopmental Disorder; Noise; Outcome; Parameter Estimation; Perception; Probability; Research; Research Priority; Rest; Risk; Role; Scanning; Sensorimotor functions; Series; Severities; Shapes; Siblings; Signal Transduction; Site; Sleep; Source; Specificity; Subgroup; System; Techniques; Testing; Theoretical model; Time; Toddler; United States National Institutes of Health; Work; autism spectrum disorder; cognitive development; cohort; design; functional MRI scan; insight; kinematics; machine learning algorithm; male; neurobiological mechanism; neurodevelopment; neuroimaging; novel; predictive signature; prospective; recruit; sensorimotor system; sex; skills; social; social communication; sound

PROJECT SUMMARY/ABSTRACT Autism Spectrum Disorders (ASD) are complex disorders manifested by qualitatively atypical social communication skills and an aberrant behavioral repertoire that vary in severity across individuals. We lack neurobiologically-grounded predictors of autism in the general population. Our studies seek to fill this critical gap in our knowledge about neurobiologically-grounded quantitative signatures that precede manifestations of ASD in toddlers recruited from the general population. We aim to (i) apply advanced computational analytic techniques to formally chart the emergence of atypical developmental trajectories, and (ii) uncover and validate neurobiologically-grounded, clinically meaningful subtypes predictive of future risk for atypical development, revolutionizing brain imaging in young children. In our previous work we have discovered that head movements during functional MRI provide an abundant source of useful movement data whose statistical features are linked to clinical and cognitive outcomes in children and adults diagnosed with ASD. Our recent studies have revealed that quantitative signatures of atypical learning trajectories can be detected as early as 1-2 months in infants at high familial risk for developing ASD. Atypical functioning of the sensorimotor system has deleterious functional consequences across diverse domains of learning and development and may contribute to ASD manifestations, in toddlers screened prospectively in the general population. Using data from the NIH-funded National Database for Autism Research (NDAR) we will test whether atypical movement variability during MRI scans during the 2nd year of life in N=212 toddlers from the general population is predictive of ASD or non-ASD outcomes (vs. typical development, TD) ascertained during the 3rd year. We will rigorously quantify key kinematic parameters during MRI scans acquired in toddlers ages 12-24 months according to different conditions, including sleeping or resting, while language is presented to sleeping toddlers, and also during a socially-orienting scan. We hypothesize that deleterious, context-incongruent signatures during the 2nd year of life in toddlers will be related subsequently to greater ASD manifestations at 36-48 months. Machine learning algorithms will be used to classify ASD, non-ASD, and TD toddlers. The overall goal of these studies is to illuminate the neurobiological basis of sensorimotor variability in toddlers from the general population and to establish that sensorimotor signatures are part and parcel of the child’s future ASD diagnosis, a finding which will have profound, transformative implications for neuroimaging methods in young children. This knowledge will provide new, early mechanistic insights into the basis of such associations recently established in children, adolescents, and adults with and without ASD, as well as in human infants, and advance Research Priorities of the NIMH.

项目总结/摘要 自闭症谱系障碍（ASD）是一种复杂的疾病，表现为定性的非典型社会性障碍。 沟通技巧和异常行为的剧目，不同的严重程度在个人。我们缺乏 神经生物学基础的自闭症预测因子。我们的研究试图填补这一关键 我们对神经生物学基础的定量特征的认识存在差距，这些特征先于 从普通人群中招募的幼儿ASD。我们的目标是（i）应用先进的计算分析 技术，正式图表出现的非典型发展轨迹，（ii）发现和验证 基于神经生物学的、有临床意义的亚型，可预测未来非典型发展的风险， 彻底改变了儿童的大脑成像在我们之前的工作中，我们发现 功能性MRI期间的运动提供了有用的运动数据的丰富来源， 这些特征与诊断为ASD的儿童和成人的临床和认知结果有关。我们最近 研究表明，非典型学习轨迹的定量特征可以早在 1-2在患有ASD的高家族风险的婴儿中，感觉运动系统的非典型功能 在不同的学习和发展领域具有有害的功能后果， 在一般人群中前瞻性筛查的幼儿中，使用的数据来自 美国国立卫生研究院资助的国家自闭症研究数据库（NDAR），我们将测试非典型运动是否 一般人群中N=212名幼儿在2岁时MRI扫描期间的变异性为 预测第3年确定的ASD或非ASD结局（与典型发育，TD）。我们将 严格量化12-24个月幼儿MRI扫描期间获得的关键运动学参数 根据不同的条件，包括睡眠或休息，而语言是呈现给睡眠 幼儿，以及社会定向扫描。我们假设，有害的，背景不一致的 在幼儿生命的第二年期间的签名将随后与更大的ASD表现相关， 36-48个月。机器学习算法将用于对ASD、非ASD和TD幼儿进行分类。的 这些研究的总体目标是阐明幼儿感觉运动变异的神经生物学基础， 并确定感觉运动特征是儿童未来重要组成部分 ASD诊断，这一发现将对神经影像学方法产生深远的变革性影响， 年幼的孩子。这些知识将提供新的，早期的机械见解的基础，这种协会 最近在患有和不患有ASD的儿童、青少年和成人以及人类婴儿中建立， 推进NIMH的研究重点。