Discovering novel predictors of minimally verbal outcomes in autism through computational modeling

拟议的项目将创建新的多维模型来表征语言前发展 导致自闭症谱系障碍儿童言语和最小言语(MV)结果的途径 (ASD)。患有自闭症的儿童在语言前交流的发展方面有明显的延迟 (PLC)技能是语言发展道路上进步的重要指标。PLC技能包括 发声、手势、共同注意和理解。多达30%的ASD儿童仍然患有MV， 当他们到达幼儿园时，即使有口语，也很少。我们的产品仍然存在重大差距 了解易使儿童保持MV的早期危险因素。特别是，还需要进一步的研究 以确定特定的转折点，这些转折点预示着不能使用口语的风险。这个 拟议的创新建模框架将评估特定语言前阶段之间的过渡 而这些转变的时机代表着MV结果的风险。至关重要的是，该项目将开发一部小说 一种量化儿童在5岁时发生MV的风险的方法，给定他们在5岁之前的PLC进展情况 发展。这样的信息可以指导和集中早期干预努力，如强化治疗 在发展和/或决定何时引入增强性或替代性沟通的某些阶段。 该方案的关键创新是利用连续时间隐马尔可夫模型来描述 18-36岁PLC在注意力、发声、手势和理解维度上的进展 几个月的ASD儿童，并确定独特的多维轨迹预测哪些儿童 在5岁时保持MV。该提案将检验这样一种假设，即在PLC之间过渡的儿童 分期越慢或遵循非典型进展模式，发生MV结局的风险越高。目标1将 开发和验证基于状态的注意力、发声、手势和 对典型发育期儿童和分别患有 ASD(活动1a)，然后构建一个多维模型，将各个模型统一到 同时检查四个维度的进展(活动1b)。模型将首先在以下位置进行验证 50名典型发育儿童的样本从6到18个月大，每3个月观察一次，然后 对100名18-24岁自闭症儿童每隔3个月观察一次的样本进行单独验证 在36个月大的时候。目标2将利用来自1b的ASD模型来确定MV状态在5处的预测因素 活动2a)，并应用生存分析方法将这些预测因素转化为 MV结果的可量化风险分数(活动2b)。 模型和基础训练数据将发布给研究社区，从而实现ASD 和发展研究人员使用新的数据集来评估他们的数据在多大程度上与 我们的，从而推进了发展科学领域，为有针对性的干预奠定了基础。

The proposed project will create novel multidimensional models to characterize the prelinguistic developmental pathways leading to verbal and minimally verbal (MV) outcomes in children with autism spectrum disorder (ASD). Children with ASD experience significant delays in the development of prelinguistic communication (PLC) skills that are important indicators of progress along a path towards language. PLC skills include vocalizations, gestures, joint attention, and comprehension. As many as 30% of children with ASD remain MV, producing very few if any spoken words by the time they reach kindergarten. Significant gaps remain in our knowledge of early risk factors that predispose children to remain MV. In particular, further research is needed to identify specific inflection points that are indicative of risk for not progressing to spoken language. The proposed innovative modeling framework will assess whether transitions between specific prelinguistic stages and the timing of these transitions represent risk for MV outcomes. Crucially, the project will develop a novel method for quantifying a child’s risk of a MV outcome at age 5 given their PLC progressions at earlier points in development. Such information could guide and focus early intervention efforts, such as intensifying therapies at certain points in development and/or deciding when to introduce augmentative or alternative communication. The key innovation in this proposal is leveraging Continuous-Time Hidden Markov Models to delineate progressions of PLC across dimensions of attention, vocalizations, gestures, and comprehension from 18-36 months in children with ASD, and to identify unique multidimensional trajectories that predict which children remain MV at age 5. The proposal will test the hypothesis that children who are transitioning between PLC stages more slowly or following atypical patterns of progression are at higher risk for MV outcomes. Aim 1 will develop and validate state-based models of development of attention, vocalizations, gestures, and comprehension in a well-characterized sample of typically developing children and separately, children with ASD (Activity 1a), and then construct a multidimensional model that unifies the individual models to simultaneously examine progressions across the four dimensions (Activity 1b). Models will be first validated on a sample of 50 typically developing children observed every 3 months from 6 to 18 months of age, and then separately validated on a sample of 100 children with ASD observed every 3 months from diagnosis at 18-24 through 36 months of age. Aim 2 will utilize the ASD model from 1b to identify predictors for MV status at 5 years in children with ASD (Activity 2a) and apply a survival analysis approach to turn these predictors into a quantifiable risk score for MV outcome (Activity 2b). The models along with the underlying training data will be released to the research community, enabling ASD and developmental researchers with novel datasets to assess the extent to which their data is consistent with ours, thus advancing the field of developmental science and laying the foundations for targeted interventions.