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: 10450733
• 负责人: Brittany Gail Travers
• 金额: $ 44.73万
• 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/10450733
• 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; Neurons; Participant; Patients; Phenotype; Population; Research; Resources; Risk; SNP array; Sampling; Scanning; Statistical Methods; Symptoms; Testing; Time; Work; autism spectrum disorder; autistic children; base; behavior observation; biomarker development; 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; neurobehavior; neurobehavioral; neuroimaging; neuropsychiatric disorder; novel; prospective; psychosocial; recruit; relating to nervous system; social; stem cell differentiation; stem cells; 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.

项目概要/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。 与没有 ASD 的人相比，患有 ASD 的人患 ADHD 的风险要高 22 倍，最近的研究 有证据表明，自闭症谱系障碍 (ASD) 与注意力缺陷多动症 (ADHD) 共同发生的几率高于任何其他精神健康障碍。 这种同时发生对个人的负面影响是巨大的；患有这两种疾病的人 （ASD/+ADHD）表现出较低的认知功能、更严重的社交障碍以及更大的适应延迟 与没有 ADHD 的自闭症谱系障碍 (ASD/-ADHD) 患者相比，其功能有所改善。这样做的总体理由 建议将基因组学、神经影像学、行为学、人类干细胞和 机器学习方法可能揭示对衰弱和衰弱背后的机制的关键见解 儿童中 ASD/+ADHD 常见同时发生。拟议工作的总体目标是确定 ASD/-ADHD 和 ASD/+ADHD 的病因学机制。我们假设患有以下疾病的儿童 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。该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） 表征诱导多能干细胞分化的神经元的分子和细胞特征 (iPSC) 由患有 ASD/-ADHD 和 ASD/+ADHD 的个体产生； 3) 识别和量化 自闭症谱系障碍 (ASD) 和多动症 (ADHD) 的基因结构重叠； 4）开发集成的机器学习模型 用于预测 ASD/-ADHD 和 ASD/+ADHD 的多模态数据。拟议研究的创新之处包括 应用最先进的神经成像（经过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 患有 ASD 年龄的儿童的症状）、iPSC（识别不同的细胞和分子特征）、新的统计数据 多表型建模和基因识别方法，以及创新的多视图机器学习 整合多模式数据来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 高度响应，因为 它涉及全面的组学方法，以显着增加我们对多个 IDD 的理解 条件以改善诊断并促进未来生物标志物的开发。获得的知识将是 意义重大，因为它可以用于对 ASD 和 ADHD 进行更强大的多模式评估， 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 的早期筛查和诊断产生重要影响，并将提供 未来生物标志物开发的基于独特生物学的目标。