# Reliable and robust causal inference approaches for effective connectivity research with fMRI data

> **NIH NIH P20** · BROWN UNIVERSITY · 2022 · $402,724

## Abstract

Neuroscientists grapple with understanding the causal mechanisms within brain networks that govern 
perception, cognitive functions, decision-making, and behavior. To understand these mechanisms, 
researchers have attempted to translate statistical associations between brain regions (so-called functional 
connectivity) into causal relationships, raising the question of whether one brain region has a direct 
influence on the physiological activity recorded in other brain regions. Such causal relationships are called 
effective connectivity. Effective connectivity is essential in learning how neural activities in different regions 
are causally related. It also provides important evidence for designing future experiments aiming at 
affecting certain cognitive outcomes by intervening on specific neural processes. Several methods have 
been developed for identifying and estimating effective connectivity, including Granger causality and 
dynamic causal modeling. However, these existing methods are vulnerable to spurious associations due to 
the shared network, temporal, and spatial dependence structures found in neuroimaging data. Moreover, 
they are not often explicit about the assumptions on unmeasured confounders, such as whether and how 
much unobserved neural activities that affect multiple brain regions are allowed when determining effective 
connectivity. These two common sources of spurious and biased findings can readily mislead our 
understanding of effective connectivity, resulting in poorly designed experiments or interventions for 
improved cognitive functions. The goal of this proposal is to develop reliable and robust causal inference 
methods to infer effective connectivity between brain regions that account for the shared dependence 
structures as well as unmeasured confounding factors. This proposed research will raise awareness of 
potential sources of bias and misleading findings in neuroimaging (e.g., fMRI) data, as well as provide 
more reliable and robust inferential tools than existing methods that are often relying on a single p-value 
from a single parameter in the presumed parametric model. This pilot research will pave the way for future 
independent funding that will further investigate effective connectivity among multiple brain regions that are 
robust to many sources of spurious findings.

## Key facts

- **NIH application ID:** 10709066
- **Project number:** 5P20GM103645-10
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Youjin Lee
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $402,724
- **Award type:** 5
- **Project period:** 2022-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10709066

## Citation

> US National Institutes of Health, RePORTER application 10709066, Reliable and robust causal inference approaches for effective connectivity research with fMRI data (5P20GM103645-10). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10709066. Licensed CC0.

---

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