Functional Data Analysis for High-Dimensional Biobehavioral Data

高维生物行为数据的功能数据分析

• 批准号: 10357949
• 负责人: Damla Senturk
• 金额: $ 35.17万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-06 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10357949
• 关键词: Age; Behavior Therapy; Behavioral; Biological; Biological Markers; Brain; Brain imaging; Brain region; Child; Clinical Trials; Cognitive; Communication; Communities; Complex; Data; Data Analyses; Data Science; Development; Diagnosis; Dimensions; Effectiveness; Electroencephalography; Exhibits; Experimental Designs; Funding; Goals; Grant; Healthcare; Heterogeneity; Imaging technology; Impaired cognition; Impairment; Infant; Intervention Trial; Investigation; Joints; Lead; Learning; Life; Literature; Longitudinal Studies; Longitudinal trends; Measures; Medical; Methodology; Modality; Modeling; Modernization; Movement; Nature; Neurodevelopmental Deficit; Neurodevelopmental Disorder; Patients; Performance; Pharmacology; Population; Process; Public Health; Research; Siblings; Social Interaction; Statistical Data Interpretation; Statistical Methods; Structure; Techniques; Time; United States National Institutes of Health; Visit; autism spectrum disorder; base; behavioral impairment; biobehavior; cognitive development; cognitive process; complex data; data modeling; flexibility; high dimensionality; high risk; imaging modality; individuals with autism spectrum disorder; insight; instrument; interest; longitudinal design; multimodal data; multimodality; novel; predictive marker; social; social attention; social communication; trend; user friendly software; visual tracking

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.

项目摘要 每59名儿童中就有1名被诊断患有自闭症谱系障碍（ASD）， 以社会交往障碍为特征的神经发育障碍， 通信我们在这笔赠款中的建议是由两项研究的动机， ASD的有前途的生物行为生物标志物模式，脑电图（EEG）和眼- 跟踪（ET）。这两项研究都收集了连续执行的EEG和ET任务的数据， 多次纵向访问。此外，模式内或跨模式的多个任务也会利用类似的 认知领域因此，即使对这些复杂数据结构的联合分析 任务，模式（EEG和ET）和纵向访问将导致最有效地利用 现有的分析技术是有限的，通常是在数据上进行的。 从一个任务一次，在一个模态。因此，我们建议一套全面的 生物行为生物标志物数据分析的统计方法， 信息来自多个任务，跨模式和纵向访问。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 对象不同于现有的多模态脑成像文献，其融合了脑- 区域相关推理，我们结合联合收割机脑成像模式（EEG）与生物行为 标记（ET），基于与共同认知域相关的任务的信息。我们 统一框架致力于跨维度和实验任务组合联合收割机信息， 提供有意义的方式来解释在较低维度中获得的信息。这些 这些发展将为数据科学和生物医学界提供新的工具， 科学调查，包括用户友好的软件，以协助医疗和公共卫生 基于生物行为多模态数据的决策。 目标。我们提出了三个具体目标：1）（任务）开发一个功能分配框架， 在模态内跨任务对所述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 来自生物标志物的多个任务的数据的联合轨迹中的性能趋势 纵向访视的模态; 3）（多模态）对整个数据进行建模， 多模式生物标志物。每个目标中的建议都依赖于通过特征进行降维 分配框架估计一组潜在的低维认知域。 然后，根据儿童对代表不同的 认知领域，有助于ASD异质性的研究。