# Inter-modal Coupling Image Analytics

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $730,192

## Abstract

PROJECT SUMMARY
Almost all brain imaging studies now collect multiple imaging modalities, in an effort to derive measures of both
structure and function from diverse imaging sequences. While quantitative data scientists have focused on
machine learning approaches for predicting outcomes using multi-modal imaging, rigorous statistical methods
for examining the relationship between imaging modalities have lagged behind. At present, the lack of statistical
methodologies for assessing inter-modal coupling (IMCo) has left investigators with ad hoc solutions that lack
statistical power and are prone to type I error, posing a threat to scientific rigor and reproducibility. In this
application, we propose robust methods that leverage subject-specific measurements and use nonlinear
modeling to address complex relationships in brain maps or networks, while accounting for important covariates
(Aim 1). Furthermore, we will develop powerful approaches for assessing whether effects of interest (e.g.,
psychopathology, development) are enriched within brain networks (Aim 2). Assessment of this coupling
between statistical associations and brain networks will capitalize upon tools from statistical genomics (e.g.,
gene set enrichment analysis) to provide principled methods for conducting enrichment analyses using high-
dimensional, personalized brain networks. Finally, we will use these tools to delineate how trans-diagnostic
executive dysfunction in youth with mental illness is related to abnormalities in structure-function coupling within
brain networks (Aim 3). To do this, we will leverage three massive data resources: the Philadelphia
Neurodevelopmental Cohort (PNC; n=1,601), the Healthy Brain Network (n=3,200), and the Human
Connectome-Development (HCP-D; n=1,300) study Taken together, the proposed work builds upon the notable
success in the first project period, promising to yield rigorous and generalizable methods for delineating the
relationships between complementary measures of brain structure and function.

## Key facts

- **NIH application ID:** 10898693
- **Project number:** 5R01MH112847-08
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Theodore Satterthwaite
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $730,192
- **Award type:** 5
- **Project period:** 2017-05-10 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10898693

## Citation

> US National Institutes of Health, RePORTER application 10898693, Inter-modal Coupling Image Analytics (5R01MH112847-08). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10898693. Licensed CC0.

---

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