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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

基本信息

批准号：
10543058
负责人：
Jed Thomas Elison
金额：
$ 74.1万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10543058
关键词：
3 year old Age Age Months Autism Diagnosis 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 Heterogeneity Individual Intervention Language Life Long-Term Effects Measures Methods Modeling Neurodevelopmental Disorder Outcome Parents Pattern Phenotype Populations at Risk Procedures Recommendation Reporting Research Research Domain Criteria Risk Sampling Scanning Stratification Subgroup Symptoms Testing Toddler Training Validation Variant autism spectrum disorder autistic children brain based case-based clinically actionable cohort communication behavior connectome design early screening follow up assessment high risk high risk population improved instrument learning algorithm neuroimaging novel novel strategies outcome prediction population based prediction algorithm prevent public health relevance repetitive behavior resilience risk stratification screening social communication symptomatology translational impact unsupervised learning

项目摘要

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）的主要障碍是我们的局限性