Parsing early emerging heterogeneity related to autism spectrum disorder

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 了解不同儿童在蹒跚学步时是如何表现出体征的，包括哪些危险症状重合 跨多个维度预测结果。我们的目标是量化行为和大脑的连接 基于风险亚型，模拟ASD症状表达的变异性 蹒跚学步的孩子然后，我们将在三岁的同一组儿童中测试这种方法的预测有效性。 以确定不同的风险特征来预测以后的认知、行为和临床特征。 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 3,000名18至24个月大的儿童，以确定一组风险特征。我们假设 每种方法都将确定一定比例的高危个体，与流行病学估计的 自闭症和相关的发育障碍(例如，语言或全球发展障碍)。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处于风险中的群体 他说，这些儿童不同于低风险儿童。所有儿童都将接受相同的大脑扫描 在Baby Connectome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。我们对300名儿童的神经成像样本将进行重新评估 在三岁时，有使用黄金标准诊断仪器的直接临床评估，以及父母 报告情况。这将使我们能够验证在18-24个月内实施的风险分析方法，以便与 目前的筛选方法，并通过有监督的预测训练来改进风险描述方法 合并行为/临床结果数据的算法。我们预计这种方法是有风险的 分层/分型以更好地模拟早期风险表型和弹性表型所固有的异质性， 这将随后改善早期识别/诊断工作。这些结果将具有翻译性 影响，因为改进的ASD早期识别方法是成功的 制定有效的、个性化的早期干预措施。

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.