# Integrative Brain Network-Based Analysis for Heterogeneous and Multimodal

> **NIH NIH R01** · UNIVERSITY OF TX MD ANDERSON CAN CTR · 2022 · $400,191

## Abstract

PROJECT SUMMARY
This proposal develops state of the art approaches for addressing challenging questions related to the
neurobiological mechanisms affecting clinical outcomes of interest in the presence of heterogeneity represented
by underlying disease sub-categories and variability in symptoms and other relevant variables across individuals.
We focus on developing integrative approaches for brain connectome based analyses, which combines the multi-
modal imaging (e.g. fMRI and diffusion MRI) of brain function and structure, clinical and behavioral measures,
while accounting for heterogeneity across samples. Our goals involve important questions in neuroscience which
have received limited or no attention so far, such as estimating dynamic brain connectivity while incorporating
brain anatomical structure, and subsequently examining which dynamic functional connections drive the clinical
outcome, accounting for heterogeneity in terms of disease sub-categories when predicting the clinical outcome
based on brain measurements which lie on an underlying brain network, and investigating differences in shapes
of white matter fiber bundles which drive the clinical outcome. To address such challenging goals, we develop
state-of-the-art statistical approaches which incorporate significant innovations and rely on multi-modal
neuroimaging data and uses biologically informed priors which yield meaningful solutions. The motivating dataset
is the Grady Trauma Project, which contains neuroimaging, behavioral, and clinical data on subjects who were
exposed to trauma and developed some degree of PTSD. We will test our approaches on an external PTSD
validation dataset from the ENIGMA-PTSD-PGC consortium. Our methodology development will include
proposing novel approaches for (a) the joint modeling of multiple graphical models using network-valued
regression; (b) using brain anatomical knowledge to inform the estimation of dynamic connectivity and
subsequently using the dynamic functional connections to predict the clinical outcome of interest; (c) developing
novel approaches for the joint estimation of multiple regression models corresponding to varying subgroups while
incorporating network information characterizing the covariates, and (d) developing Bayesian approaches for 3-
dimensional shape estimation for fiber tracts in the brain using anatomically informed priors, and subsequently
using the shapes of the estimated fiber bundles to predict the clinical outcomes of interest. We also develop a
robust strategy for the validation of the proposed methods and we also provide an outline for developing software
and sharing them openly with researchers and interested parties. This application addresses several clinical
significant questions in neuroimaging research which have not been explored before due to the lack of state of
the art statistical methodology, and is expected to make important methodological, scientific, clinical and
translational contributions.
.

## Key facts

- **NIH application ID:** 10457493
- **Project number:** 5R01MH120299-04
- **Recipient organization:** UNIVERSITY OF TX MD ANDERSON CAN CTR
- **Principal Investigator:** Suprateek kundu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $400,191
- **Award type:** 5
- **Project period:** 2021-07-26 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10457493

## Citation

> US National Institutes of Health, RePORTER application 10457493, Integrative Brain Network-Based Analysis for Heterogeneous and Multimodal (5R01MH120299-04). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10457493. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*