Parsing early emerging heterogeneity related to autism spectrum disorder

解析与自闭症谱系障碍相关的早期出现的异质性

基本信息

批准号：
10543058
负责人：
Jed Thomas Elison
金额：
$ 74.1万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10543058
关键词：
3 year old Age Age Months Autism Diagnosis Behavior Behavioral Biological Brain Brain imaging Categories Child Classification Clinical Clinical assessments Cognitive Communities Complex Data Detection Development Developmental Disabilities Diagnosis Diagnostic Dimensions Disease Early Intervention Early identification Epidemiology Etiology Goals Heterogeneity Individual Intervention Language Life Long-Term Effects Measures Methods Modeling Neurodevelopmental Disorder Outcome Parents Pattern Phenotype Populations at Risk Procedures Recommendation Reporting Research Research Domain Criteria Risk Sampling Scanning Stratification Subgroup Symptoms Testing Toddler Training Validation Variant autism spectrum disorder autistic children brain based case-based clinically actionable cohort communication behavior connectome design early screening follow up assessment high risk high risk population improved instrument learning algorithm neuroimaging novel novel strategies outcome prediction population based prediction algorithm prevent public health relevance repetitive behavior resilience risk stratification screening social communication symptomatology translational impact unsupervised learning

项目摘要

A major impediment to early identification and intervention for autism spectrum disorder (ASD) is our limited understanding of how different children present signs as toddlers, including what risk symptoms coincide across multiple dimensions to predict outcome. Our objectives are to quantify behavioral and brain connectivity based subtypes of risk that model the variability of ASD symptom expression in a community sample of toddlers. We will then test the predictive validity of this approach in the same cohort of children at three years of age in order to identify risk profiles that differentially predict later cognitive, behavioral, and clinical features. First, we will implement two unsupervised data-driven computational approaches in a community sample of 3000 children between 18-24 months old in order to characterize clusters of risk profiles. We hypothesize that each approach will identify a proportion of high-risk individuals consistent with epidemiological estimates of ASD and associated developmental disabilities (e.g., language or global DD). Based on our preliminary data, we anticipate that ~300 children will be identified by these data-driven risk-profiling methods. We also hypothesize that distinct patterns of structural and functional connectivity will distinguish groups of at-risk children and that these groups will differ from low-risk children. All children will be scanned with the same brain imaging sequences and procedures implemented in the Baby Connectome Project and will be compared to data from 100 low-risk children from that project. Our neuroimaging sample of 300 children will be reassessed at age three with direct clinical assessment using gold-standard diagnostic instruments as well as parent report. This will allow us to validate the risk profiling approach implemented at 18-24 months, to compare with a current screening approach, and to refine the risk profiling approach with supervised training of prediction algorithms that incorporates behavioral/clinical outcome data. We expect this method for risk stratification/subtyping to better model the heterogeneity inherent to the early at-risk and resilient phenotypes, which will subsequently improve early identification/diagnosis efforts. These outcomes will have translational impact because improved methods for early identification in ASD are necessary for the successful development of efficacious, personalized early interventions.

自闭症谱系障碍 (ASD) 早期识别和干预的一个主要障碍是我们的有限了解不同的孩子在幼儿时期如何表现出迹象，包括哪些风险症状是一致的跨多个维度来预测结果。我们的目标是量化行为和大脑连接基于风险的子类型，模拟社区样本中 ASD 症状表达的变异性幼儿。然后，我们将在三年内的同一组儿童中测试这种方法的预测有效性年龄，以确定风险概况，以差异化预测以后的认知、行为和临床特征。首先，我们将在社区样本中实现两种无监督数据驱动的计算方法 3000 名 18-24 个月大的儿童，以描绘风险状况的集群特征。我们假设每种方法都将确定一定比例的高风险个体，其与流行病学估计相一致自闭症谱系障碍 (ASD) 和相关的发育障碍（例如语言或整体发育障碍）。根据我们的初步数据，我们预计，这些数据驱动的风险分析方法将识别出约 300 名儿童。我们也假设结构和功能连接的不同模式将区分高危人群儿童，并且这些群体与低风险儿童不同。所有儿童都将使用相同的大脑进行扫描婴儿连接组项目中实施的成像序列和程序将与来自该项目的 100 名低风险儿童的数据。我们将重新评估 300 名儿童的神经影像样本三岁时，使用金标准诊断仪器以及家长进行直接临床评估报告。这将使我们能够验证在 18-24 个月实施的风险分析方法，以与当前的筛选方法，并通过预测的监督训练来完善风险分析方法结合行为/临床结果数据的算法。我们预计这种方法的风险分层/亚型分析，以更好地模拟早期风险和弹性表型固有的异质性，这将随后改善早期识别/诊断工作。这些成果将产生转化影响，因为改进自闭症谱系障碍 (ASD) 早期识别方法对于成功制定有效的、个性化的早期干预措施。