# Integrating facial coding of expressive behavior and functional MRI: A multimodal approach linking momentary affective experience to concurrent changes in brain activity during drug craving

> **NIH NIH R21** · PENNSYLVANIA STATE UNIVERSITY, THE · 2020 · $199,825

## Abstract

Functional neuroimaging has become a widely used approach for studying substance use and addiction. This
is particularly true in the area of research on cigarette smoking, which remains one of the largest threats to
public health in the world. Neuroimaging research on the use of cigarettes and other substances has focused
largely on characterizing brain activity associated with drug craving (an intense urge or desire to use drugs).
This reflects the prevailing view that craving plays a central role in the maintenance of addiction and serves as
a major barrier to treatment and recovery. Over the past several years, researchers using neuroimaging to
study craving have benefitted from a number of significant methodological advances (e.g., increasingly
sophisticated data analysis methods). However, the methods that are used for subjective affective experience
have changed very little, and investigators must largely rely upon the same self-report measures that have
been available since the earliest days of neuroimaging craving research. Used in isolation, self-report
measures typically lack the sensitivity and precision that are needed to relate momentary affective experience
to craving-related brain activity – an important limitation given the intimate relationship between craving and
affect. The goal of the proposed research is to address this barrier to progress in the field by developing a
novel method for measuring subjective affective experience in neuroimaging craving research that harnesses
the unique strengths of facial expression analysis. Specifically, this method entails recording participants' facial
expressions using a magnetic resonance imaging (MRI) compatible camera and then using facial coding
analysis to derive a time course of affective reactions from the videos. By providing a way to unobtrusively
capture moment-to-moment changes in affect, facial coding is an ideal method for connecting fluid affective
reactions to dynamic changes in brain activity in the context of craving. The proposed strategy of integrating
the assessment of facial expressions of affect with neuroimaging methods will be tested in a sample of adult
daily smokers. Facial expressions will be recorded as participants complete a functional MRI (fMRI) protocol
that has proven to be highly effective for provoking strong cigarette cravings. The specific aims of the project
are: 1) To demonstrate the feasibility of measuring affect by analyzing video recordings of facial expressions
displayed during fMRI; and 2) To demonstrate that moment-to-moment changes in affect are meaningfully
associated with ongoing brain activity under conditions designed to produce robust craving. If successful, the
proposed project will provide a foundation for using this new method to explore a variety of questions that are
currently very difficult to address (e.g., characterizing how affect changes dynamically in relation to brain
activity when smokers attempt to regulate their craving). A...

## Key facts

- **NIH application ID:** 9901499
- **Project number:** 5R21DA045853-02
- **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE
- **Principal Investigator:** Stephen Jeffrey Wilson
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $199,825
- **Award type:** 5
- **Project period:** 2019-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9901499

## Citation

> US National Institutes of Health, RePORTER application 9901499, Integrating facial coding of expressive behavior and functional MRI: A multimodal approach linking momentary affective experience to concurrent changes in brain activity during drug craving (5R21DA045853-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9901499. Licensed CC0.

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