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

通过计算模型发现自闭症最低限度语言结果的新预测因素

基本信息

批准号：
10676845
负责人：
NANCY CAROLINE BRADY
金额：
$ 53.23万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-05 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10676845
关键词：
Affect Age Age Months Agreement Attention Behavior Child Child Development Child Language Classification Communication Communities Comprehension Computer Models Data Data Set Development Diagnosis Dimensions Disease model Early Intervention Education Failure Foundations Gestures Individual Intervention Joints Knowledge Language Learning Life Linguistics Literature Measurement Methods Modeling Nature Outcome Pathway interactions Pattern Predisposing Factor Research Research Personnel Risk Risk Factors Sampling Science Survival Analysis Testing Time Training Validation alternative communication autism spectrum disorder autistic children data-driven model effective intervention experience high risk innovation joint attention kindergarten language outcome markov model minimal risk minimally verbal model development novel predictive modeling public health relevance risk prediction model skill acquisition skills verbal vocalization

项目摘要

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.

拟议的项目将创建新颖的多维模型来表征前语言发展自闭症谱系障碍儿童获得言语和最低限度言语 (MV) 结果的主要途径（自闭症谱系障碍）患有自闭症谱系障碍的儿童在语言前交流的发展方面存在显着的延迟。 (PLC) 技能是 PLC 技能进步的重要指标，其中包括。多达 30% 的 ASD 儿童的发声、手势、共同注意力和理解力仍处于 MV 状态。当他们到达幼儿园时，他们的口语能力即使有也很少。特别是，需要进一步研究导致儿童罹患 MV 的早期危险因素。识别表明不进展到口语的风险的特定拐点。拟议的创新建模框架将评估特定前语言阶段之间是否存在过渡至关重要的是，这些转变的时机代表了 MV 结果的风险。考虑到早期阶段的 PLC 进展，量化儿童 5 岁时发生 MV 结果的风险的方法此类信息可以指导和集中早期干预工作，例如强化治疗。在发展的某些时刻和/或决定何时引入增强或替代沟通。该提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 岁期间 PLC 在注意力、发声、手势和理解力方面的进展患有自闭症谱系障碍（ASD）儿童的几个月，并确定独特的多维轨迹来预测哪些儿童 5 岁时仍保持 MV。该提案将检验以下假设：在 PLC 之间过渡的儿童进展缓慢或遵循非典型进展模式的阶段 MV 结果的风险较高。开发并验证基于状态的注意力、发声、手势和发展的模型理解典型发育儿童的良好特征样本，以及单独患有发育障碍的儿童 ASD（活动 1a），然后构建一个多维模型，将各个模型统一起来同时检查四个维度的进展（活动 1b）。对 50 名正常发育的儿童进行抽样调查，从 6 个月到 18 个月大，每 3 个月观察一次，然后对 100 名患有自闭症谱系障碍 (ASD) 儿童的样本进行单独验证，自 18 至 24 日诊断后每 3 个月观察一次目标 2 将利用 1b 中的 ASD 模型来确定 5 岁时 MV 状态的预测因子。患有自闭症谱系障碍 (ASD) 儿童的年数（活动 2a），并应用生存分析方法将这些预测因素转化为 MV 结果的可量化风险评分（活动 2b）。带有底层训练数据的模型将发布给研究社区，使 ASD 和发展研究人员使用新颖的数据集来评估他们的数据与我们的，从而推进发展科学领域并为有针对性的干预措施奠定基础。