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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.

提出的项目将创建新的多维模型来表征前语言发展