# Functional Data Analysis for High-Dimensional Biobehavioral Data

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $351,699

## Abstract

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.

## Key facts

- **NIH application ID:** 10596470
- **Project number:** 5R01MH122428-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Damla Senturk
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $351,699
- **Award type:** 5
- **Project period:** 2020-05-06 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10596470

## Citation

> US National Institutes of Health, RePORTER application 10596470, Functional Data Analysis for High-Dimensional Biobehavioral Data (5R01MH122428-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10596470. Licensed CC0.

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