Statistical approaches to improving functional connectivity estimates with an application to autism

摘要 功能磁共振成像(FMRI)被用来估计大脑区域之间的相关性。去- 尽管fmri提供了许多关于大脑功能的见解，但field目前正在经历一种重复性。 危机。例如，自闭症谱系障碍(ASD)被认为是大脑连接紊乱的结果， 但ASD文献包含了fl关于低连接和高连接的令人担忧的报告。参与者头部运动 是一个关键因素，排除高运动量的参与者可以减少运动的影响。然而， 在儿科fMRI研究中，运动质量控制删除了超过50%的数据。在ASD中，最严重的是- 配对的儿童往往被排除在外--我们只描述了自闭症谱系中有限的一部分。此外, 方法学的发展和实证研究的重点是静态测量的功能连通性。 孩子们在某些任务中活动较少，比如看电影。任务期间的动态连接可以提供 对大脑连通性疾病的关键洞察具有更大的影响规模，但流行的方法不利用任务 模型fi设置期间的信息。这笔赠款的总体目标是开发减少 在功能连通性研究中偏向和提高Effi效率。我们的建议是基于静息状态的功能磁共振成像 来自肯尼迪·克里格研究所和约翰·霍普金斯大学的数百名儿童对自闭症的研究。我们 将在马库斯自闭症中心收集的独立测试数据集中评估外部效度，并 在埃默里大学，我们还将在一部带有社交互动的电影中进行一项关于参与度的研究。至 为了实现我们的目标，我们提出了以下几点建议：1)提出了一种缺失数据去重发现的方法 ASD的功能连接签名。这一目标解决了由于运动质量控制而产生的采样偏差。2) 开发用于功能连通性和运动的信号分解的因果中介框架。这一目标 将用于区分神经和运动对ASD功能连通性的贡献。3)开发一部小说 任务动态功能连通性的协方差回归模型。这一目标将考验科学fic 假设ASD儿童参与的神经基础不同于典型的发育迟缓儿童 不同的孩子。我们将开发软件，以便建议的方法可以广泛应用于神经成像 研究包括神经障碍和精神健康。完成这些目标将提供以下工具： 提高功能连接性研究的重复性；2)揭示ASD的神经基础。这可以 协助制定治疗方法和教育战略。

ABSTRACT Functional magnetic resonance imaging (fMRI) is used to estimate the correlations between brain regions. De- spite the many insights into brain function provided by fMRI, the field is currently experiencing a reproducibility crisis. For instance, autism spectrum disorder (ASD) is thought to be the result of disordered brain connections, but the ASD literature contains conflicting reports of both hypo- and hyper-connectivity. Participant head motion is a crucial factor, and the exclusion of high-motion participants can reduce the impacts of motion. However, motion quality control removes more than 50% of data in pediatric fMRI studies. In ASD, the most severely im- paired children tend to be removed – we are characterizing a limited part of the autism spectrum. In addition, methodological development and empirical studies have focused on functional connectivity measured at rest. Children move less during some tasks, such as watching a movie. Dynamic connectivity during a task may offer crucial insights into diseases of brain connectivity with larger effect sizes, but popular methods do not utilize task information during model fitting. The overall objective of this grant is to develop statistical methods that decrease bias and improve efficiency in functional connectivity studies. Our proposal is motivated by a resting-state fMRI study on ASD with hundreds of children from the Kennedy Krieger Institute and Johns Hopkins University. We will assess external validity in an independent test dataset that will be collected at the Marcus Autism Center and Emory University, where we will also conduct a study of engagement during a movie with social interactions. To achieve our objective, we propose the following: 1) Develop a missing data method for deconfounding to reveal functional connectivity signatures of ASD. This aim addresses sampling biases due motion quality control. 2) Develop a causal mediation framework for signal decomposition of functional connectivity and motion. This aim will be used to disentangle neural versus motion contributions to function connectivity in ASD. 3) Develop a novel covariance regression model for dynamic functional connectivity during a task. This aim will test the scientific hypothesis that the neural underpinnings of engagement differ in children with ASD compared to typically devel- oping children. We will develop software so that the proposed methods can be broadly applied to neuroimaging studies including neurological disorders and mental health. Completing these aims will 1) provide tools that will improve reproducibility in functional connectivity studies and 2) reveal the neural underpinnings of ASD. This can aid in the development of treatments and educational strategies.