# Scalable Bayesian methods for big imaging data analysis

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $314,685

## Abstract

ABSTRACT!
This proposal will address the most timely and important issues in statistical analysis of big imaging data. Our
project is motivated by "The Adolescent Brain Cognitive Development (ABCD) Study”, which is the largest
long-term study of brain development and child health in the United States and is funded by the National
Institutes of Health (NIH). Innovative aspects of this proposal are: 1) We develop a new Bayesian image-on-
vector regression model with novel sparse and smooth Gaussian process priors. It enables to perform
association analysis between high-resolution images of brain activity and high-dimensional vectors of social
environmental factors and clinical variables. To the best of our knowledge no existing methods can efficiently
and jointly analyze high-resolution images and high-dimensional vectors of covariates simultaneously under a
systematic modeling framework; 2) We develop a new Bayesian scalar-on-image neural network model with
sparse, smooth, and spatially-varying coefficients. This new model has great potential to make better
predictions about the risk of an adolescent initiating substance use compared to all existing methods; and more
importantly, it will identify important imaging biomarkers that are associated with substance use patterns. This
will provide a better understanding of the pathology of substance use initiation; 3) We propose a Bayesian
model for high-dimensional mediation analysis of multimodality imaging data by combining image-on-vector
regression and scalar-on-image regression with modifications. Under the potential outcome framework, !
we will define the direct effects of environmental factors/electronic health records on psychopathology, as well
as their indirect effects that are mediated through the changes in brain functions and/or structures. 4) We
develop scalable posterior computation algorithms for all of the proposed models. These efficient computation
tools will enable the possibility to apply the statistical methods in the clinical and translational research and
applications. Our methods can address two key questions about adolescent brain cognitive development: 1)
they will identify important childhood experiences and social environmental factors, such as sports, video
games, social media, unhealthy sleep patterns, and smoking, that affect brain development; 2) understand the
inferences of brain development on the risk of substance use initiation and patterns, including detailed quantity,
frequency, route of administration, and co-use patterns. !
!

## Key facts

- **NIH application ID:** 10451601
- **Project number:** 5R01DA048993-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Timothy D Johnson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $314,685
- **Award type:** 5
- **Project period:** 2020-09-30 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10451601

## Citation

> US National Institutes of Health, RePORTER application 10451601, Scalable Bayesian methods for big imaging data analysis (5R01DA048993-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10451601. Licensed CC0.

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