Discovering Molecular and Neural Biomarkers of Social and Language Development in ASD Toddlers

发现 ASD 幼儿社交和语言发展的分子和神经生物标志物

• 批准号: 10862025
• 负责人: ERIC COURCHESNE
• 金额: $ 66.01万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10862025
• 关键词: Address; Adult; Affect; Affective; Affective Symptoms; Age; Autopsy; Behavioral; Biological; Biological Markers; Biological Process; Brain; Brain imaging; Cell model; Child; Clinical; Communication; Data; Data Set; Development; Diagnosis; Disease; Ethnic Origin; Functional Magnetic Resonance Imaging; Functional disorder; Gene Expression; General Population; Genes; Gestational Age; Glean; Grant; Heterogeneity; Image; Impairment; Intake; Knowledge; Language; Language Development; Learning; Leukocytes; Licensing; Life; Linear Regressions; Link; Magnetic Resonance Imaging; Maps; Measures; Methods; Modality; Molecular; Molecular Medicine; Molecular Profiling; National Institute on Deafness and Other Communication Disorders; Neuronal Dysfunction; Neurons; Outcome; Parents; Pathway interactions; Patients; Pattern; Process; Prognosis; Prognostic Marker; Psychologist; Psychometrics; Publications; Race; Reproducibility; Role; Sampling; Severities; Signal Pathway; Signal Transduction; Social Development; Social Interaction; Social outcome; Speech; Standardization; Structure; Subgroup; Symptoms; Testing; Time; Toddler; Treatment outcome; Validation; Work; autism spectrum disorder; brain overgrowth; cell type; checkup examination; clinical predictors; clinical prognosis; clinically actionable; cohort; computer studies; design; differential expression; disability; disorder subtype; early screening; fetus cell; in vivo; indexing; individual patient; innovation; insight; language outcome; molecular subtypes; morphometry; multimodality; nerve stem cell; neural; neuroimaging; novel; outcome prediction; patient prognosis; patient subsets; pediatrician; postnatal; precision medicine; predict clinical outcome; prenatal; prenatal disorder; prevent; prognostic; response; risk variant; social; social communication; social deficits; transcriptomics; visual tracking

Social affect, social processing, and social communication symptoms differing in degrees of severity appear in the first years of life in autism spectrum disorder (ASD), giving rise to pervasive, lifelong challenges to patients, parents, and therapists. Lack of verified early-age biological and clinical ASD subtypes prevents accurate prediction of clinical progression and treatment outcome. ASD social affective symptoms are ASD specific signs and can be indexed by motherese eye tracking (ET); social differences are undoubtedly caused by neural dysfunction such as those found by our grant, but the molecular pathobiology underlying neural and ET social heterogeneity are not well understood. Subtypes of fMRI neural responses to social affective language are known from our grant work, as are atypical cortical patterning for a subgroup of ASD toddlers with poor language and social outcomes. Our recent work identifies gene expression features associated with such subgroups, and dysregulated signaling pathways are related to social symptom severity. Missing is how these various imaging, social and gene findings are connected in ASD subtypes, and what molecular drivers underlie social neural activity. AIM 1 - discover underlying molecular drivers of ASD social neural and social ET subtypes. Using an unsupervised data-driven precision medicine method (Similarity Network Fusion (SNF)) for patient subtyping, we will integrate multimodality social clinical, social eye tracking, social speech activation fMRI, MRI-based cortical parcellation, and transcriptomic data from a large new cohort of toddlers to discover molecular dysregulations underlying subtypes of ASD social neural dysfunction. Replication analyses will use our existent NIDCD cohort as an independent sample. Subtypes will be tested for robustness and reproducibility. Features in each modality that differentiate ASD subtypes will be identified via tests of each pair of subtypes over each data modality. AIM 2 - prenatal molecular processes and cell types associated with ASD neural-social subtypes. Using normative BrainSpan expression data to map prenatal temporal-spatial activity of pathways disrupted in ASD subtypes, we will learn when and where disrupted pathways in ASD subtypes are normally upregulated during prenatal development. Top differentially expressing (DE) genes in each disrupted pathway will be analyzed for enrichment in GO biological processes. Using normative cell type- specific gene markers, we will test whether DE genes in ASD neural-social subtypes enrich prenatal and postnatal cell type-specific markers, and determine which neural-social subtypes strongly express a recently identified multi-pathway DE-ASD network that is over-active in ASD neural progenitors and neurons. AIM 3 – prognosis prediction from ASD subtypes. For toddlers in each ASD subtype, we use Bayesian multimodality sparse linear regression learning to determine which transcriptomic, brain imaging, eye tracking and treatment features in each subtype predict each child's outcome social and language 1.5 years after clinical intake. This opens avenues for clinical actionable prognostic outcome subtype markers for individual patients.

社会影响，社会处理和社会交流症状在严重性外观程度上有所不同 自闭症谱系障碍（ASD）生命的头几年，给患者带来了普遍，终生的挑战， 父母和治疗师。缺乏经过验证的早期生物学和临床ASD亚型可防止准确 预测临床进展和治疗结果。 ASD受社会影响的符号是ASD特定符号 并且可以通过母亲的眼睛跟踪（ET）索引；社会差异无疑是由神经引起的 功能障碍，例如我们的赠款发现的功能障碍，但是神经和社会的分子病理生物学 异质性不太了解。 fMRI神经对社会影响语言的反应的亚型是已知的 从我们的赠款工作中，对于语言差的ASD幼儿子组的非典型皮质图案也是如此 社会成果。我们最近的工作确定了与此类亚组相关的基因表达特征，并且 信号通路失调与社会症状严重程度有关。缺少的是这些各种成像的方式， 社会和基因的发现与ASD亚型有关，以及哪些分子驱动因素是社会中立的基础 活动。目标1-发现ASD社会神经和社会ET子类型的基本分子驱动因素。使用 一种无监督的数据驱动的精密医学方法（相似性网络融合（SNF）） 亚型，我们将整合多模式的社会临床，社交眼动追踪，社会语音激活fMRI， 基于MRI的皮层拟层和来自大量新幼儿队列的转录组数据可发现 ASD社会神经功能障碍的亚型的分子失调。复制分析将使用 我们现有的NIDCD队列作为独立样本。亚型将测试鲁棒性和 可重复性。在每种模式中，将通过每种测试确定分化ASD子类型的特征 每种数据模式上的一对子类型。 AIM 2-产前分子过程和与相关的细胞类型 ASD神经社会亚型。使用普通的brainspan表达数据来映射产前临时空间 在ASD亚型中破坏的路径的活动，我们将学习何时何地破坏ASD中的路径 在产前发育过程中，通常会上调亚型。最高表达（de）基因的顶部 将分析每个中断的途径，以在GO生物过程中富集。使用正常的细胞类型 - 特定的基因标记，我们将测试ASD神经社会亚型中的DE基因是否富含产前和 产后细胞类型特异性标记，并确定哪些神经社会亚型强烈表达最近 鉴定出在ASD神经元和神经元中过度活跃的多条纹DE-ASD网络。目标3 - ASD亚型预测预测。对于每个ASD亚型的幼儿，我们使用贝叶斯多模式 稀疏线性回归学习以确定哪种转录组，大脑成像，眼睛跟踪和治疗 每个子类型的特征都可以预测每个孩子的结果社交和语言，临床摄入后1。5年。这 为个别患者打开了可为临床可操作的预后结果亚型标记的途径。