Investigating quantitative signatures of autism in toddlers
研究幼儿自闭症的定量特征
基本信息
- 批准号:10531781
- 负责人:
- 金额:$ 38.78万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:
项目摘要
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.
项目摘要/摘要
自闭症谱系障碍(ASD)是一种复杂的障碍,表现为定性的非典型社会
沟通技巧和异常行为,严重程度因人而异。我们缺少
一般人群中自闭症的神经生物学预测因子。我们的研究试图填补这一关键
我们对以神经生物学为基础的定量特征的认识存在差距,这些特征出现在
从普通人群中招募的学步儿童的自闭症。我们的目标是(I)应用高级计算分析
正式绘制非典型发展轨迹出现的图表的技术,以及(2)发现和验证
神经生物学基础的,临床上有意义的亚型,预示着非典型发展的未来风险,
革命性地改变了幼儿的大脑成像。在我们之前的工作中,我们发现了头部
功能磁共振期间的运动提供了丰富的有用运动数据来源,其统计数据
这些特征与被诊断为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,非典型学习轨迹的量化特征最早可以被检测到
家族性ASD高危婴儿1-2个月。感觉运动系统的非典型功能
在学习和发展的不同领域产生有害的功能后果,并可能
在一般人群中进行前瞻性筛查的幼儿中,导致自闭症的表现。使用来自
NIH资助的国家自闭症研究数据库(NDAR)我们将测试非典型运动
来自普通人群的N=212名幼儿在出生2年期间的MRI扫描的变异性是
在第3年确定ASD或非ASD结果的预测值(与典型发展相比,TD)。我们会
严格量化12-24个月幼儿在MRI扫描中获得的关键运动学参数
根据不同的情况,包括睡眠或休息,而语言则呈现为睡眠
蹒跚学步的孩子,也是在面向社会的扫描期间。我们假设有害的,与背景不一致的
蹒跚学步儿童第二年的签名将与随后更严重的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。这个
这些研究的总体目标是阐明幼儿感觉运动可变性的神经生物学基础。
并确定感觉运动特征是儿童未来不可或缺的一部分
ASD诊断,这一发现将对脑部疾病的神经成像方法产生深远的、革命性的影响
年幼的孩子。这一知识将为这种联系的基础提供新的、早期的机械性见解
最近在患有和不患有自闭症的儿童、青少年和成人以及人类婴儿中建立了,
和推进NIMH的研究重点。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Kristina Denisova其他文献
Kristina Denisova的其他文献
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{{ truncateString('Kristina Denisova', 18)}}的其他基金
Investigating quantitative signatures of autism in toddlers
研究幼儿自闭症的定量特征
- 批准号:
10349536 - 财政年份:2020
- 资助金额:
$ 38.78万 - 项目类别:
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