Using complex video stimuli to elucidate atypical brain functioning in ASD

摘要：自闭症谱系障碍(ASD)的大脑特征的发现和记录 多年来，这一直是一个非常令人向往但仍难以实现的目标。Identified面临的一个关键挑战是 不同地点之间、参与者之间和参与者内部的各种级别和可变性来源-- 模糊了对这些可复制神经特征的搜索，使生物标记物和 破坏了认知和神经机制的阐明。在这份续期申请中，我们建议 分离和量化这些可变性来源的一系列研究。我们将获得一个新的fMRI数据集 这在一定程度上与上一个供资期间积累的数据是连续的，并具有三个主要特点。首先，我们 将扫描具有ASD和匹配控制的参与者，同时观看具有丰富叙事的复杂视频 内容。对视频的唤起反应限制了参与者内部和之间的可变性以及数据收集 网站，如强大的试点数据所证明的，和自然主义的视频更接近处理需求 复杂的现实社会环境。第二，我们将使用密集采样、纵向获取、高密度的 高质量的神经成像数据，将允许精确、稳定和可靠地测量个人的大脑 功能。第三，我们将在两个地点(印第安纳大学和加州理工大学)收集主要数据，以确保 更广泛的概括性。使用机器学习技术，目标1将学习大脑中的位置以及何时、在 对这段视频的回应是，ASD患者与对照组参与者的差异最大。扩展到集团之外- 水平平均值，我们还将采取维度方法将大脑差异与表型差异联系起来，以及 一种识别与ASD亚组的存在一致的变异的聚类方法。在目标2中，我们将 利用这些结果以及最先进的计算机视觉和语音算法来量化 视频的刺激特征，唤起这些神经差异，无论是在群体层面还是在群体层面 个体，并研究它们与表型差异的关系。我们的综合功能 视频的分解将查询高层语义特征、人脸等对象级特征和低层语义特征 使感知特征平整。最后，Aim 3将共享这项工作的所有产品--例如，原材料和加工产品 数据、获取工具、注释、分析脚本-以现代格式显示OpenNeuro、NDA和Github (例如，使用投标格式，并以fMRIprep和MRIQC作为处理和质量保证工具) 他们最大限度地为其他人所接近。独一无二的是，此次数据发布将经过验证，并将在第三次发布时重新fi 爱荷华大学对控制参与者的较小样本进行了研究。因此，此续订应用程序将构建 利用当前资助期的工具、数据和进展，并利用最先进的计算机 识别引起非典型神经的视听刺激特征的视觉和神经成像分析方法 自闭症患者的活动性。该项目将提供对神经和认知差异的新见解 ASD，并帮助指导未来对基于神经成像的标记物的研究。

SUMMARY: The discovery and refinement of brain-based signatures of autism spectrum disorder (ASD) has for many years been a highly desired, but as yet elusive, goal. One key challenge identified has been that numerous levels and sources of variability — between sites, between participants, and within participants — obscure the search for these reproducible neural signatures, complicating the search for biomarkers and undermining the elucidation of cognitive and neural mechanisms. In this renewal application we propose a sequence of studies to dissociate and quantify these sources of variability. We will acquire a new fMRI dataset that is partly continuous with data accrued during the prior funding period and has 3 key features. First, we will scan participants with ASD and matched controls while they watch complex videos with rich narrative content. Evoked responses to videos constrain variability within and across participants and data collection sites, as borne out by strong pilot data, and naturalistic videos better approximate the demands of processing complex real-world social situations. Second, we will use densely-sampled, longitudinally-acquired, high- quality neuroimaging data that will permit precise, stable, and reliable measurements of an individual's brain function. Third, we will collect primary data at two sites (Indiana University and Caltech) in order to ensure broader generalizability. Using machine learning techniques, Aim 1 will learn where in the brain and when, in response to the video, individuals with ASD diverge most from control participants. Extending beyond group- level averages, we will also take a dimensional approach to link brain differences to phenotypic variation, and a clustering approach to identify variation consistent with the presence of ASD subgroups. In Aim 2, we will leverage these results together with state-of-the-art computer vision and speech algorithms to quantify the stimulus features of the videos that evoke these neural differences, both at the level of the group and of the individual, and examine their relationship to phenotypic differences. Our comprehensive feature decomposition of the videos will query high-level semantic features, object-level features like faces, and low- level perceptual features. Finally Aim 3 will share the all the products of this work — e.g., raw and processed data, acquisition tools, annotations, analysis scripts — on OpenNeuro, NDA, and Github in modern formats (e.g., using BIDS format, and with fMRIprep and MRIQC as processing and quality assurance tools) to make them maximally accessible to others. Uniquely, this data release will be validated and refined at yet a third site, the University of Iowa, on a smaller sample of control participants. This renewal application will thus build upon tools, data, and progress from the current funding period, and capitalize on state-of-the-art computer vision and neuroimaging analysis methods to identify audiovisual stimulus features that evoke atypical neural activity in individuals with ASD. This project will provide new insight into neural and cognitive differences in ASD, and help guide future investigations toward neuroimaging-based markers.