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Functional Data Analysis for High-Dimensional Biobehavioral Data

高维生物行为数据的功能数据分析

基本信息

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
项目类别：
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

项目摘要

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.

项目总结