# Rethinking the Neural Correlates of Uncertain Threat Anticipation with a Statistical Learning Approach

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2022 · $186,731

## Abstract

PROJECT ABSTRACT
Anxiety disorders are some of the most common and debilitating psychiatric diagnoses,
affecting ~1 in 3 people worldwide. Despite the enormous impact on global well-being, key
concepts that are critical to understanding these disorders remain poorly defined in the extant
literature. Although heightened sensitivity to "uncertain threat anticipation" is regarded as a core
contributor to anxiety and anxiety disorders and Threat Uncertainty is a core construct that cuts
across models of anxiety in the NIMH RDoC framework, there is no ground-truth definition of
"uncertainty". Previous studies have found that anticipation of temporally “uncertain” threats, as
compared to temporally “certain” threats, are associated with increases self-reported anxiety,
startle-responses, and brain activation in anxiety-related brain regions. In this proposal, we will
test an alternate explanation for these results using a computational, statistical-learning
approach. In particular, we suggest that lab-based assessments of uncertain anticipation have
confounded “uncertainty” with alterations in the probability of a threat given that it has not
already occurred, i.e. the hazard-rate. Here, building on our team’s expertise in anxiety and
decision-making, we will test the hypothesis that it hazard-rate, and not uncertainty per se,
accounts for increases in self-reported anxiety and persistence (Aim 1), as well as alterations in
anxiety-related BOLD responses in anxiety-relevant brain regions (Aim 2). To this end, we have
developed a novel paradigm that allows for a 2x2 design where we manipulate uncertainty
(high/low) and hazard-rate (high-low). In contrast to existing theories, which predict a main
effect of uncertainty, we predict a main effect of hazard-rate on all relevant measures. Relevant
measures include self-reported anxiety, persistence in a threatening environment, and fMRI
measures of brain activation. We will contrast this paradigm with previous paradigms that have
confounded uncertainty and hazard-rate to provide a more precise interpretation of extant
findings. This theory-driven computational approach to understanding uncertain anticipation has
the potential to provide a “course correct” for the field, refine our understanding of anxiety, and
inform the development of new treatments.

## Key facts

- **NIH application ID:** 10426704
- **Project number:** 1R21MH129851-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Andrew S Fox
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $186,731
- **Award type:** 1
- **Project period:** 2022-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10426704

## Citation

> US National Institutes of Health, RePORTER application 10426704, Rethinking the Neural Correlates of Uncertain Threat Anticipation with a Statistical Learning Approach (1R21MH129851-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10426704. Licensed CC0.

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