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.

项目摘要/摘要 自闭症谱系障碍（ASD）经常与注意力缺陷/多动障碍（ADHD）共同出现。 与没有ASD的人相比，ASD患有ADHD的风险高22倍，最近 有证据表明，与其他任何精神健康障碍相比，ASD与ADHD的同时发生的速度更高。 这种共同发生对个体的负面影响是很大的。那些出现这两种疾病的人 （ASD/+ADHD）表现出较低的认知功能，更严重的社会障碍和更大的自适应延迟 比没有ADHD的ASD（ASD/-ADHD）的人的功能。总体原理 建议是基因组，神经成像，行为，人类干细胞和人类干细胞的多学科整合 机器学习方法可能会揭示对衰弱和衰弱的机制的关键见解 儿童ASD/+ADHD的常见共同出现。拟议工作的总体目标是确定 ASD/-ADHD和ASD/+ADHD的病因机制。我们假设孩子 ASD/+ADHD将具有独特的遗传，分子，细胞，脑结构和神经行为特征 与患有ASD/-ADHD的儿童相比。该假设将通过四个特定目的进行检验：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指标； 2） 表征与诱导多能干细胞区分开的神经元的分子和细胞特征 （IPSC）由ASD/-ADHD和ASD/+ADHD的个体产生； 3）识别和量化 ASD和ADHD重叠的遗传体系结构； 4）开发集成的机器学习模型 多模式数据以预测ASD/-ADHD和ASD/+ADHD。拟议研究的创新包括 应用最新的神经影像学（优化以促进难以扫描的人群中的脑成像）， 前瞻性纵向设计（以多动症的发展过程中的个体差异说明 与ASD年龄儿童一样），IPSC（识别不同的细胞和分子特征），新的统计 多形型建模和基因识别的方法，以及创新的多视机学习 整合多模式数据以识别功能基因组元件和基因调节的方法 基于ASD/+ADHD出现的网络。该项目对IDDRC RFA的反应很高， 它涉及全面的方法，以显着提高我们对超过单个IDD的理解 改善诊断并促进未来生物标志物发展的条件。获得的知识将是 意义重大，因为它可以用来告知对ASD和ADHD的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）生物学测定相结合。这些 调查结果将对ASD和ADHD的早期筛查和诊断具有重要意义，并将提供 未来生物标志物开发的不同基于生物学的目标。