# Advancing understanding of neural representations of threat perception through a novel predictive coding framework

> **NIH NIH F32** · NORTHEASTERN UNIVERSITY · 2021 · $66,390

## Abstract

PROJECT SUMMARY
Anxiety disorders affect an estimated 6.7% to 7.3% of people globally each year and incur a large burden on
people’s lives. To address this urgent problem, it is necessary to develop a better understanding of the neural
bases of subjective experiences in anxiety, including threat perception. Translational neuroscience has focused
on animal models of defensive behavior involving a core set of regions. Although these animal models and the
human subjects research they inspired have led to advances in treatment of anxiety, the mapping between neural
mechanisms and subjective experience remains poorly understood. The set of regions found to support
defensive behavior in animal models does not appear to be involved in all instances of fear or anxiety. The
current project overcomes this barrier by integrating current models of anxiety with predictive coding models of
the mind and brain. Incorporating predictive coding into models of anxiety will offer a better understanding of
how neural activity relates to subjective experiences important to anxiety (e.g., threat perception). The project
tests two parallel hypotheses about neural representation of threat perception suggested by predictive coding
models: that neural representations of threat perception are content-specific (Aim 1) and that neural
representations of threat perception depend on expectations (Aim 2). Using a single design, we manipulate
content-specificity and expectations to test these two hypotheses in parallel. We use fMRI to measure brain
activity and use self-report and peripheral psychophysiology to measure subjective experiences of threat
perception. To the extent that participants find stimuli threatening (as indexed by self-report and
psychophysiology), we hypothesize that we will observe relatively content-specific neural representations of
threat. We also hypothesize that neural representations of threat will differ under conditions of expectation vs.
expectancy violation. The effect of expectation may impact content-specific neural activity or activity in a core
set of regions. The knowledge gained from the proposed project has the potential improve understanding of the
mapping between neural activity and subjective experiences. Relevant for translational neuroscience, a better
understanding of the psychological and neurobiological mechanisms of anxiety will be critical to closing the gap
between laboratory research and more effective treatments for anxiety. More broadly, predictive coding models
are models of basic brain function. Thus, the predictive coding model we propose offers a new theoretical
framework that is generalizable to psychiatric illnesses involving disordered threat perception (e.g.,
schizophrenia) and other affective disorders (e.g., depression, bipolar disorder).

## Key facts

- **NIH application ID:** 10240282
- **Project number:** 5F32MH122062-02
- **Recipient organization:** NORTHEASTERN UNIVERSITY
- **Principal Investigator:** Kent M Lee
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $66,390
- **Award type:** 5
- **Project period:** 2020-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10240282

## Citation

> US National Institutes of Health, RePORTER application 10240282, Advancing understanding of neural representations of threat perception through a novel predictive coding framework (5F32MH122062-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10240282. Licensed CC0.

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