# Statistical methods for structural and functional integration in multi-modal neuroimaging data

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $484,432

## Abstract

Abstract
Neuropsychiatric disorders, such as autism and schizophrenia, affect millions of people
worldwide and place a considerable burden on both patients and family members. Existing
treatments for these disorders have limited efficacy, in part due the varied clinical
manifestations, and to our narrow understanding of the impacted neural processes, particularly
at the system (i.e., network) level. Two key elements of networks are the underlying
infrastructure or physical connections between elements and the functional signaling between
entities that rides on top of this infrastructure. Recent advancements in noninvasive imaging
have given us the ability to quantify structural and functional relationships in the brain via
diffusion MRI, resting-state functional MRI, respectively. The size and scope of datasets
measuring network structure and function are increasing in neuroimaging, and other domains,
which heightens the need for new statistical frameworks that make full use of the data.
Our goal is to develop frameworks for the analysis of structure-function integration in large-scale
and complex networks, applied to neuroimaging studies, but also broadly applicable. This
proposal will introduce three analytic paradigms: Bayesian network modeling that uses a priori
structure-function knowledge for simultaneous network anomaly detection and clinical severity
prediction; density regression using optimal transport theory; and end-to-end prediction using
deep neural networks. In our application, infrastructure will be measured via dMRI, while
function will be measured rs-fMRI. Each of our frameworks will provide a unique means to
integrate these distinct imaging modalities, while also respecting the unique information
provided by each data type. We also propose a unique software development effort that creates
an application program interface to core software and implementations as software as as a
service hosted on cloud platforms.

## Key facts

- **NIH application ID:** 10445053
- **Project number:** 5R01EB029977-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** BRIAN Scott CAFFO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $484,432
- **Award type:** 5
- **Project period:** 2021-07-05 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10445053

## Citation

> US National Institutes of Health, RePORTER application 10445053, Statistical methods for structural and functional integration in multi-modal neuroimaging data (5R01EB029977-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10445053. Licensed CC0.

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