This study aims to learn more about avoidance behavior in people with anxiety, using mathematical models of decision-making processes and decoded neural signals of threat imminence.

Researchers are investigating anxiety-related behavior and brain function in people with and without anxiety. Investigators are also looking at how behavior and brain function during tasks in the lab relate to avoidance in their daily lives. The investigators will also test whether changing how people avoid things in a behavioral task affects how people avoid things in their everyday life.

Current learning theories of anxiety propose that disrupted fear learning underlie anxiety disorders. This suggests that treatments like exposure therapy work by changing learned threat values. However, empirical data does not support these models where changes in fear conditioning lead to symptom changes and later reductions of avoidance behavior. Instead, recent findings suggest that avoidance behavior can be a mechanism on its own, and reductions in avoidance behavior both precede and predict improvements in anxiety symptoms. To target avoidance, a working theory of the processes underlying avoidance behavior is needed. Recently, Markov Decision Process (MDP) models have been used to measure threat expectancy, which has the advantage of capturing the difference between avoidance and fear learning as mechanisms in anxiety disorders. Initial MDP models that demonstrate the rise of avoidance behavior show promise; however, additional non-behavioral information is needed to fit these models in humans.

The researcher's main hypothesis is that MDP models, augmented with decoded neural signals of threat imminence, can characterize and modify anxiety disorder-related avoidance behavior in and outside of the laboratory.

Aim 1: Adults unselected for psychopathology (120, assessed twice): develop a brain signature of threat imminence (from a predictive model independently trained on fMRI data from other threat imminence tasks) and combine with task behavior to create an MDP model of avoidance behavior.

Aims 2 and 3: A separate set of participants with clinical anxiety and maladaptive avoidance (N=163, assessed four times) will complete an MDP-based learning task assessing avoidance during fMRI scanning and quantify differences in MDP-modeled behavior and test if the magnitude of these task-based differences predicts between-and within-person differences in the severity of real-world avoidance behavior.

Multivariate predictors of functional magnetic resonance imaging (fMRI) data can decode latent values and enhance the computational fit of the MDP models. To identify these latent values, previously validated neural signatures of a threat-imminence model will be used. In the threat-imminence model, the same brain regions (e.g., amygdala, vmPFC), but different ensembles and neural populations, are involved in different stages of threat imminence, and these patterns do not differ between humans with different levels of clinical anxiety, allowing for a neurobiologically supported approach to creating latent values of threat. Therefore, researchers aim to use MDP models, augmented with decoded neural signals of threat imminence, to characterize and support a new mechanism (avoidance behavior) of symptom change in anxiety and modify anxiety disorder-related avoidance behavior.

Inclusion Criteria:

• Ability to comprehend written and spoken English
• Ability to provide informed consent
• Estimated intelligence quotient (IQ) >70.
• Clinically significant anxiety symptoms (scoring above clinical cutoff on at least one Inventory of Depression and Anxiety Symptoms (IDAS) anxiety-related subscale and/or OASIS total)
• Significant anxiety-related avoidance (scoring 2 or above on a 0-4 scale, indicating at least "occasional" avoidance) on the avoidance-related question on the Overall Anxiety Severity and Impairment Scale (OASIS)
• Functional impairment, defined as at least moderate impairment in one WHO Disability Assessment Schedule (WHODAS 2.0) domain related to anxiety.

Exclusion Criteria:

• Individuals younger than 22 or older than 55,
• Individuals with an IQ < 70,
• A lifetime history of neurological disorder or brain damage,
• Contraindications for undergoing MRI scanning,
• A lifetime history or diagnosis of psychosis or bipolar disorder,
• Current substance use intoxication or withdrawal,
• Severe risk of suicide, or
• Recent (<3 months) changes in psychotropic medicine or psychotherapy treatment,
• Ineligible at the PI's discretion, are excluded.

Researchers will share individual de-identified data, including demographic, clinical, behavioral performance on tasks, naturalistic self-reports of avoidance behavior, GPS and accelerometer data, and structural and functional MRI data.

Data will be deposited starting 12 months after the award begins (August 2026) and will be deposited every six months thereafter. The analysis code will be published upon the manuscript acceptance.

Data will be uploaded to the NIMH Data Archive and subject to their access policies. Code will be uploaded to GitHub and/or Open Science Foundation (osf.io) and will be publicly available.