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.

Public Health Relevance

Modern studies collect complex patient level information over serially administered tasks of a biobehavioral modality, across multiple modalities and at potentially multiple longitudinal visits. Our proposal characterizes recorded data as high-dimensional highly structured functional objects, and develops a principled statistical framework for its joint analysis. The proposed methodological framework will provide interpretation and rigorous inference from these data structures, which will help guide medical and health care decisions.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Wouhib, Abera
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University of California Los Angeles
Biostatistics & Other Math Sci
Schools of Public Health
Los Angeles
United States
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