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

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)并存。 患有自闭症的人患ADHD的风险是没有自闭症的人的22倍，最近 有证据表明，自闭症与ADHD的共生率高于其他任何精神健康障碍。 这种共同发生对个体的负面影响是巨大的；那些同时出现这两种疾病的人 (ASD/ADHD)表现为认知功能低下，社交障碍更严重，适应能力延迟更大 功能优于无ADHD的ASD患者(ASD/-ADHD)。这样做的总体理由是 建议将基因组、神经成像、行为、人类干细胞和 机器学习方法可能揭示导致衰弱和肥胖的机制 ASD/ADHD在儿童中常见并存。拟议工作的总体目标是确定 ASD/-ADHD和ASD/ADHD的发病机制。我们假设患有这种疾病的儿童 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。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预测因子；2) 诱导多能干细胞分化为神经元的分子和细胞特性研究 (IPSCs)由ASD/-ADHD和ASD/ADHD患者产生；3)识别和量化 ASD和ADHD的重叠遗传结构；以及4)开发一个机器学习模型，将 多模式数据预测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(优化以便于在难以扫描的人群中进行脑成像)， 前瞻性纵向设计(解释ADHD发展过程中的个体差异 症状作为ASD年龄的儿童)、IPSCs(识别不同的细胞和分子图谱)、新的统计数据 用于多表型建模和基因识别的方法以及创新的多视点机器学习 一种集成多模式数据以识别功能基因组元件和基因调控的方法 这些网络是ASD/ADHD出现的基础。该项目高度响应IDDRC RFA，因为 它涉及全面的方法，以显著增加我们对不止一个IDD的理解 改善诊断和促进未来生物标记物发展的条件。所获得的知识将是 意义重大，因为它可以用来为ASD和ADHD提供更强大的多模式评估， 将行为观察与技术先进(但高度可行)的生物检测相结合。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 未来生物标记物开发的不同生物学靶点。

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.