Functional Data Analysis for High-Dimensional Biobehavioral Data

项目概要 大约五分之一的儿童被诊断患有自闭症谱系障碍 (ASD) 以社交互动障碍为特征的神经发育障碍 沟通。我们在这笔赠款中的提议是受到两项关于两个最重要的研究的启发 自闭症谱系障碍（ASD）、脑电图（EEG）和眼电图等有前途的生物行为生物标志物模式 跟踪（ET）。这两项研究都从连续执行的 EEG 和 ET 任务中收集数据， 多次纵向访问。此外，模式内或跨模式的多个任务会利用类似的方法 认知领域。因此，即使对这些复杂的数据结构进行联合分析 任务、模式（EEG 和 ET）和纵向访问将导致最有效地利用 现有的信息，当前的分析技术是有限的，通常是在数据上进行 在一种模式内一次完成一项任务。因此，我们提出了一套全面的 用于分析整个生物行为生物标志物数据的统计方法，借用 来自多种任务、跨模式和纵向访问的信息。我们的建议 依赖于 EEG 和 ET 数据作为高维高度结构化函数的表征 对象。与现有的多模态脑成像文献不同，现有的多模态脑成像文献融合了脑- 区域相关推理，我们将脑成像模式（EEG）与生物行为结合起来 标记（ET），基于与共同认知领域相关的任务信息。我们的 统一框架致力于将跨维度的信息和实验任务结合起来 提供在较低维度解释所获得信息的有意义的方法。这些 发展将为数据科学和生物医学界提供新的工具 科学研究，包括用户友好的软件，以协助医疗和公共卫生 基于生物行为多模式数据的决策。 目标。我们提出三个具体目标：1）（任务）开发一个特征分配框架 对模态内跨任务的高维生物行为数据进行建模； 2） （纵向）扩展目标 1 的特征分配模型以考虑纵向 一个生物标记物的多个任务的数据联合轨迹的性能趋势 纵向访问的方式； 3）（多模式）对整个数据进行建模 多模式生物标志物。每个目标的提案都依赖于通过特征来降维 估计一组底层低维认知域的分配框架。 然后根据代表不同因素的多个因素对儿童进行聚类 认知领域，有助于自闭症谱系障碍异质性的研究。

PROJECT SUMMARY About 1 in 59 children are diagnosed with autism spectrum disorder (ASD), a neurodevelopmental disorder characterized by impairments in social interaction and communication. Our proposals in this grant are motivated by two studies on the two most promising biobehavioral biomarker modalities of ASD, electroencephalography (EEG) and eye- tracking (ET). Both studies collect data from serially administered EEG and ET tasks, over multiple longitudinal visits. In addition, multiple tasks within or across modalities tap into similar cognitive domains. Hence, even though joint analysis of these complex data structures across tasks, modalities (EEG and ET) and longitudinal visits would lead to the most efficient use of the available information, current analysis techniques are limited and are usually carried out on data from one task at a time, within a modality. Therefore, we propose a comprehensive set of statistical methods for the analysis of biobehavioral biomarker data in its entirety, borrowing information from multiple tasks, across modalities and over longitudinal visits. Our proposal relies on characterization of EEG and ET data as high-dimensional highly structured functional objects. Different from existing multimodal brain imaging literature, which fuses data for brain- region related inference, we combine a brain imaging modality (EEG) with a biobehavioral marker (ET), based on information on tasks that are related to common cognitive domains. Our unified framework strives to combine information across dimensions and experimental tasks to provide meaningful ways of interpreting the gained information in lower dimensions. These developments will provide the data science and biomedical community with novel instruments of scientific investigation, including user friendly software, to assist medical and public health decisions based on biobehavioral multimodal data. Aims. We propose three specific aims: 1) (Task) To develop a feature allocation framework for modeling the high-dimensional biobehavioral data across tasks within a modality; 2) (Longitudinal) To extend the feature allocation modelling of Aim 1 to account for longitudinal performance trends in the joint trajectories of data from multiple tasks of a biomarker within a modality across longitudinal visits; 3) (Multimodal) To model the data in its entirety across multimodal biomarkers. Proposals in each aim rely on dimension reduction through a feature allocation framework in estimating a set of underlying low-dimensional cognitive domains. Children are then clustered according to their loadings on multiple factors representing different cognitive domains, contributing to the study of heterogeneity in ASD.