How Children with ASD Develop ADHD over Time: An Integrated Analysis through the Lenses of Functional Genomics, Stem Cells, Brain Imaging, and Neurobehavior

自闭症儿童如何随着时间的推移发展为多动症：通过功能基因组学、干细胞、脑成像和神经行为的综合分析

• 批准号: 10678937
• 负责人: Brittany Gail Travers
• 金额: $ 44.9万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10678937
• 关键词: 7 year old; Address; Age; Attention deficit hyperactivity disorder; Autism Diagnosis; Behavior; Behavioral; Behavioral Assay; Biological Assay; Biology; Brain; Brain imaging; Brain scan; Cell Line; Child; Clinic; Clinical; Data; Development; Developmental Course; Developmental Disabilities; Diagnosis; Disease; Early Diagnosis; Electrophysiology (science); Elements; Etiology; Exhibits; Future; Gene Expression; Generations; Genes; Genetic; Genomic Segment; Genomics; Genotype; Goals; Head; Heritability; Human; Image; Impairment; Individual; Individual Differences; Infrastructure; Intellectual and Developmental Disabilities Research Centers; Intervention; Knowledge; Link; Machine Learning; Mental disorders; Modality; Modeling; Molecular; Molecular Profiling; Morphologic artifacts; Morphology; Motion; National Institute of Mental Health; Neurodevelopmental Disorder; Neuronal Differentiation; Neurons; Participant; Patients; Phenotype; Population; Research; Resources; Risk; SNP array; Sampling; Scanning; Specific qualifier value; Statistical Methods; Symptoms; Testing; Time; Work; autism spectrum disorder; autistic children; behavior observation; behavior prediction; biomarker development; clinical translation; cloud based; cognitive function; cohort; comorbidity; early childhood; early screening; evidence base; functional genomics; gene network; gene regulatory network; genetic architecture; genome wide association study; human stem cells; imaging modality; improved; individuals with autism spectrum disorder; induced pluripotent stem cell; innovation; insight; lens; longitudinal design; machine learning model; machine learning prediction; multidisciplinary; multimodal data; multimodality; multiple data types; nerve stem cell; neural; neurobehavior; neurobehavioral; neuroimaging; neuropsychiatric disorder; novel; prospective; psychosocial; recruit; social; stem cell differentiation; stem cells; structural imaging; transcriptomics

PROJECT SUMMARY/ABSTRACT Autism spectrum disorder (ASD) frequently co-occurs with attention-deficit/hyperactivity disorder (ADHD). Individuals with ASD have a 22 times greater risk of having ADHD compared with those without ASD, and recent evidence suggests that ASD co-occurs with ADHD at a higher rate than with any other mental health disorder. The negative impact of this co-occurrence on the individual is substantial; those presenting with both disorders (ASD/+ADHD) show lower cognitive functioning, more severe social impairment, and greater delays in adaptive functioning than individuals presenting with ASD without ADHD (ASD/-ADHD). The overall rationale of this proposal is that a multidisciplinary integration of genomic, neuroimaging, behavioral, human stem cell, and machine learning approaches may reveal key insights into the mechanisms underlying the debilitating and common co-occurrence of ASD/+ADHD in children. The overall objective of the proposed work is to identify the etiological mechanisms underlying ASD/-ADHD and ASD/+ADHD. We hypothesize that children with ASD/+ADHD will have unique genetic, molecular, cellular, brain structural, and neurobehavioral features compared to children with ASD/-ADHD. This hypothesis will be tested through four specific aims: 1) to identify prospective longitudinal behavioral and neuroimaging predictors of ASD/+ADHD compared to ASD/-ADHD; 2) to characterize molecular and cellular features of neurons differentiated from induced pluripotent stem cells (iPSCs) generated from individuals with ASD/-ADHD and ASD/+ADHD; 3) to identify and quantify the overlapping genetic architectures for ASD and ADHD; and 4) to develop a machine learning model integrating multi-modal data to predict ASD/-ADHD and ASD/+ADHD. Innovations of the proposed study include the application of state-of-the-art neuroimaging (optimized to facilitate brain imaging in difficult-to-scan populations), a prospective longitudinal design (to account for individual differences in the developmental course of ADHD symptoms as children with ASD age), iPSCs (to identify distinct cellular and molecular profiles), novel statistical methods for multi-phenotype modeling and gene identification, and an innovative multiview machine learning approach that integrates multi-modal data to identify the functional genomic elements and gene regulatory networks that underlie the emergence of ASD/+ADHD. This project is highly responsive to the IDDRC RFA, as it involves comprehensive -omic approaches to markedly increase our understanding of more than a single IDD condition to improve diagnosis and to facilitate future biomarker development. The knowledge gained will be significant because it can be used to inform a far more powerful multi-modal assessment of ASD and ADHD that integrates behavioral observations with technically advanced (but highly feasible) biological assays. These findings will have important implications for early screening and diagnosis of ASD and ADHD and will provide distinct biology-based targets for future biomarker development.

项目总结/摘要 自闭症谱系障碍（ASD）经常与注意力缺陷/多动障碍（ADHD）同时发生。 患有ASD的人患ADHD的风险是没有ASD的人的22倍， 有证据表明，ASD与ADHD的并发率高于任何其他精神健康疾病。 这种并发症对个体的负面影响是巨大的;那些同时患有两种疾病的人 (ASD/+ADHD）表现出较低的认知功能，更严重的社会障碍，以及更大的适应性延迟。 ASD/-ADHD（ASD/-ADHD）的患者。这种做法的总体原理是 建议是基因组学、神经影像学、行为学、人类干细胞和 机器学习方法可以揭示对衰弱和 ASD/+ADHD在儿童中的常见并发症。拟议工作的总体目标是确定 ASD/-ADHD和ASD/+ADHD的病因机制。我们假设， ASD/+ADHD将具有独特的遗传、分子、细胞、大脑结构和神经行为特征 与ASD/-ADHD儿童相比。这一假设将通过四个具体目标进行检验：1）确定 与ASD/-ADHD相比，ASD/+ADHD的前瞻性纵向行为和神经影像学预测因子; 2） 表征从诱导多能干细胞分化的神经元的分子和细胞特征 （3）鉴定和量化从患有ASD/-ADHD和ASD/+ADHD的个体产生的iPSC; ASD和ADHD的重叠遗传架构;以及4）开发一个机器学习模型， 多模态数据来预测ASD/-ADHD和ASD/+ADHD。拟议研究的创新之处包括 应用最先进的神经成像（优化以促进难以扫描人群的脑成像）， 前瞻性纵向设计（考虑ADHD发展过程中的个体差异 症状与ASD年龄的儿童），iPSC（以确定不同的细胞和分子谱），新的统计 多表型建模和基因识别的方法，以及创新的多视图机器学习 一种整合多模态数据以识别功能基因组元件和基因调控的方法， ASD/+ADHD出现的基础网络。该项目对IDDRC RFA反应非常积极，因为 它包括综合的组学方法，以显著提高我们对多种IDD的认识 条件，以改善诊断和促进未来的生物标志物的发展。获得的知识将是 重要的是，它可以用来为ASD和ADHD的更强大的多模式评估提供信息， 将行为观察与技术先进（但高度可行）的生物测定相结合。这些 研究结果将对ASD和ADHD的早期筛查和诊断具有重要意义，并将提供 为未来生物标志物的开发提供独特的生物学靶点。