# Causal Inference for Neuroimaging

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2020 · $292,176

## Abstract

Abstract: New technologies such as functional magnetic resonance imaging (fMRI) have aided our ability to
understand human brain function. Distinguishing causal from spurious relationships is of great interest and
importance for both basic neuroscience and applications in medicine, drug development, and clinical practice.
Neuroscientists want to know how experimental stimuli affect brain function, how neural activity in different
brain regions are causally linked, and how neural activity mediates the relationship between a stimulus and an
outcome. Researchers apply various statistical procedures (e.g., Granger causality, dynamic causal models,
structural equation models, and directed graphical models) to fMRI data, interpreting the resulting associations
as effects, often inappropriately. This is a major problem. The previous funding cycle of this grant laid the
groundwork for a principled approach to casual inference in neuroscience research, employing the potential
outcomes notation used in the statistical literature on causal inference. Here we seek to extend the frame-
work developed there to better study mediation, where neural activity in one or more brain regions mediates
a treatment-outcome relationship, and effective connectivity, where activations in different regions are causally
linked. To begin, we define new causal effects and construct a new whole brain model that will facilitate the
study of localization, mediation, and effective connectivity at the regional level under assumptions more realistic
than those typically made in modeling fMRI data. Further, we create a new statistical method for conducting high-dimensional mediation analysis that identifies networks that may mediate the relationship between a
treatment and outcome. Finally, building on the literature on instrumental variables and structural equation models, we develop new methods for studying mediation and effective connectivity after the application of external
neurostimulation using recently developed technologies such as transcranial magnetic stimulation (TMS) and
transcranial direct current stimulation (tDCS) that are starting to see widespread use in both theoretical neuroscience and applied contexts. We apply the methods to fMRI data from studies of post-traumatic stress disorder,
thermal pain and social evaluation.

## Key facts

- **NIH application ID:** 9938548
- **Project number:** 5R01EB016061-08
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Martin Lindquist
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $292,176
- **Award type:** 5
- **Project period:** 2013-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9938548

## Citation

> US National Institutes of Health, RePORTER application 9938548, Causal Inference for Neuroimaging (5R01EB016061-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9938548. Licensed CC0.

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