Parsing ASD Heterogeneity: Neuroendophenotypes of Social Attention and Sensory Responsivity

项目描述 尽管在被诊断患有自闭症谱系障碍的个体之间观察到巨大的差异 （自闭症谱系障碍），迄今为止大多数研究都将自闭症谱系障碍视为一个单一的病症，将患有自闭症谱系障碍的个体与 神经典型控制。这种方法阻碍了我们在阐明神经生物学机制方面的进展 引起自闭症谱系障碍 (ASD) 症状的疾病，同时也削弱了转化研究的潜力 为自闭症谱系障碍的“精准医疗”做出贡献。在这个项目中，我们迈出了关键的一步来剖析重要的 通过结合最先进的成像方法和新颖的解释方法，观察自闭症谱系障碍的异质性 用于遗传易感性，以及大量独特的男性样本的深层表型特征 患有 ASD 的女性，这是我们最近更新的 ACE 网络 (MH100028) 的一部分。利用 该队列中已收集的综合分析（即表型分析、基因分型、MRI、EEG）以及我们的 参与人类连接组项目 - 开发（MH109589），在这里我们将收集并分析 丰富的基于大脑的测量数据集，具有无与伦比的分辨率和质量，以表征个体 大脑网络特性的差异，并研究这些差异与大量表型测量的关系 深入感兴趣的关键领域。使用静息态 fMRI 和创新的 fMRI 激活任务作为神经 通过社交和感官反应性的分析，我们将研究功能连接和大脑反应如何 相关的神经回路在个体内部和个体之间共同变化，以确定非典型反应性 感觉刺激影响社会相关刺激的神经处理，并评估神经元的不同程度 社会和感觉反应的内表型与功能连接和行为的改变有关 表型。基于我们之前的成像遗传学工作，我们还将研究多基因风险和风险如何 ASD 相关多态性的遗传变异影响大脑功能、连接性以及核心 ASD 症状。我们的首要假设是，不同的和共同的神经内表型都将被识别 在我们的样本中，基于不同的大脑功能和连接性指标，这些将映射到 不同维度的社会和感觉异常表现在神经和行为层面。我们进一步 预计较高的多基因风险将预测大脑活动、连接和神经功能的日益异常的模式 总体症状严重程度，而 ASD 相关多态性的遗传变异会选择性地 调节表达这些自闭症谱系障碍风险基因的大脑回路的大脑功能和连接性。由 采用（a）尖端成像方法来检查大脑功能和连接性，（b）创新 将社会关注和动机与感觉处理非典型性联系起来的范式，（c）新方法 将遗传风险与神经和行为表型相结合，以及（d）复杂的数据分析策略 合理地对我们的自闭症谱系障碍样本进行分层，这项研究将进一步加深我们对神经机制的理解 揭示 ASD 的潜在异质性，并最终为更加个性化和有效的干预措施提供信息。

PROJECT DESCRIPTION Despite the tremendous variability observed across individuals diagnosed with Autism Spectrum Disorder (ASD), most research to date has treated ASD as a unitary condition, comparing individuals with ASD to neurotypical controls. This approach has hindered our progress in unraveling the neurobiological mechanisms that give rise to ASD symptomatology and it also undermines the potential of translational research to contribute to `precision medicine' in ASD. In this project, we take a critical step toward dissecting the significant heterogeneity observed in ASD by combining state-of-the-art imaging methods, novel approaches to account for genetic susceptibility, and a deep phenotypic characterization of a large and unique sample of males and females with ASD that we curated as part of our recently renewed ACE Network (MH100028). Capitalizing on the comprehensive assays already collected in this cohort (i.e., phenotyping, genotyping, MRI, EEG) and our involvement in the Human Connectome Project - Development (MH109589), here we will collect and analyze a rich dataset of brain-based measures of unparalleled resolution and quality in order to characterize individual differences in brain network properties and examine how these relate to a vast phenotypic battery of measures tapping into key domains of interest. Using resting-state fMRI and innovative fMRI activation tasks as neural assays of social and sensory responsivity, we will examine how functional connectivity and brain responses in associated neural circuits co-vary within and between individuals in order to determine how atypical reactivity to sensory stimuli impacts neural processing of socially relevant stimuli, and assess how distinct neural endophenotypes of social and sensory responsivity relate to altered functional connectivity and behavioral phenotypes. Building upon our prior imaging-genetics work, we will also examine how polygenic risk, and risk genetic variants on ASD-associated polymorphisms, influence brain function, connectivity, as well as core ASD symptoms. Our overarching hypothesis is that both distinct and shared neuroendophenotypes will be identified across our sample based on different brain function and connectivity metrics and that these will map onto varying dimensions of social and sensory atypicalities manifested at the neural and behavioral level. We further expect that higher polygenic risk will predict increasingly aberrant patterns of brain activity, connectivity, and overall symptom severity, whereas genetic variance on ASD-associated polymorphisms will selectively modulate brain function and connectivity in brain circuits where these ASD risk genes are expressed. By employing (a) cutting-edge imaging methods to examine brain function and connectivity, (b) innovative paradigms to relate social attention and motivation to sensory processing atypicalities, (c) novel approaches to integrate genetic risk with neural and behavioral phenotypes, and (d) sophisticated data-analytic strategies to sensibly stratify our ASD sample, this research will further our understanding of the neural mechanisms underlying heterogeneity in ASD and ultimately inform more personalized and efficacious interventions.