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

• 批准号: 10321552
• 负责人: Jed Thomas Elison
• 金额: $ 74.3万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10321552
• 关键词: 3 year old; Age; Age-Months; Autism Diagnosis; Base of the Brain; 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; Gold; Heterogeneity; Individual; Intervention; Language; Life; Long-Term Effects; Measures; Methods; Modeling; Neurodevelopmental Disorder; Outcome; Parents; Pattern; Phenotype; Procedures; Recommendation; Reporting; Research; Research Domain Criteria; Risk; Sampling; Scanning; Stratification; Subgroup; Supervision; Symptoms; Toddler; Training; Validation; Variant; autism spectrum disorder; autistic children; base; case control; case-based; clinically actionable; cohort; communication behavior; connectome; design; early screening; follow up assessment; high risk; improved; instrument; learning algorithm; neuroimaging; novel; novel strategies; outcome prediction; population based; prediction algorithm; predictive test; prevent; public health relevance; repetitive behavior; risk stratification; screening; social communication; symptomatology; translational impact; unsupervised learning

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.

早期识别和干预自闭症谱系障碍（ASD）的一个主要障碍是我们有限的 了解不同的儿童如何表现出蹒跚学步的迹象，包括哪些风险症状相吻合 来预测结果。我们的目标是量化行为和大脑的连接 基于风险的亚型，对社区样本中ASD症状表达的变异性进行建模， 幼儿然后，我们将在同一组儿童中测试这种方法的预测有效性， 以确定差异预测后期认知，行为和临床特征的风险概况。 首先，我们将在一个社区样本中实现两种无监督的数据驱动计算方法， 3000名18-24个月大的儿童，以确定风险特征的集群。我们假设 每种方法都将确定一部分高危人群， ASD和相关的发育障碍（例如，语言或全局DD）。根据我们的初步数据， 我们预计，通过这些数据驱动的风险分析方法，将确定约300名儿童。我们也 假设结构和功能连接的不同模式将区分风险组， 儿童，这些群体将与低风险儿童不同。所有的孩子都将被扫描同一个大脑 在婴儿连接组项目中实施的成像序列和程序，并将与 来自该项目的100名低风险儿童的数据。我们将对300名儿童的神经影像学样本进行重新评估 在三岁时，使用金标准诊断仪器进行直接临床评估， 次报告.这将使我们能够验证在18-24个月时实施的风险分析方法， 目前的筛选方法，并通过监督预测培训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/分型以更好地模拟早期风险和弹性表型固有的异质性， 这随后将改善早期识别/诊断工作。这些成果将具有 影响，因为ASD早期识别的改进方法对于成功 制定有效的个性化早期干预措施。