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.

项目总结/文摘