# Neuro-computational predictors of treatment responsiveness in trauma-exposed Veterans. > **NIH VA I01** · VA SAN DIEGO HEALTHCARE SYSTEM · 2024 · — ## Abstract While evidence-based treatments (EBTs) for PTSD are effective at reducing trauma-related anxiety symptoms, about half to two thirds of trauma-exposed Veterans do not fully recover during treatment and maintain their PTSD diagnosis. Anhedonia, i.e., a reduced interest and engagement in rewarding activities, is prevalent in trauma-exposed Veterans and is associated with including higher PTSD severity and poorer response to psychiatric treatment. Impaired reward sensitivity is therefore likely to play a critical role in treatment responsiveness in Veterans. However, to date, the degree to which such altered reward sensitivity impacts PTSD treatment responsiveness has not been tested. To test this hypothesis, the proposed study will combine computational modeling and event-related functional magnetic resonance imaging (fMRI) to assay reward processing function in Veterans at the end of Cognitive Processing Therapy (CPT), and test the usefulness of such markers in predicting treatment responsiveness. Computational modeling, particularly in concert with neuroimaging, provides detailed mechanistic insights into complex cognitive processes, which can predict clinical outcomes more accurately than standard behavioral and neuroimaging analysis. We will capitalize on this approach to delineate robust predictors of treatment response in trauma-exposed Veterans. A total of 186 trauma-exposed Veterans will be recruited immediately upon enrolling in CPT. They will complete a full clinical assessment and two multi-arm bandit (MAB) tasks (in classic and social conditions, to be compared in exploratory analyses), in which they must choose on each trial from among a set of options with unknown reward probabilities, with the goal of maximizing total rewards. Concurrent brain activity will be measured in a subset of 93 Veterans who will complete the task while undergoing fMRI. A Bayesian learning model will be applied to participants’ decisions to derive individual-level parameters representing a) individuals’ perceived stability of the unknown reward rates in the environment and b) the degree to which their model- based expectations of reward influence their choices. Neural activation parametrically associated with trial-to- trial model-based reward expectations and associated prediction errors (i.e., difference between expected and observed reward) will be extracted. All participants will complete follow-up clinical and behavioral assessments immediately after treatment and 3 months after treatment. Computational parameters and model-based neural activations will be tested as predictors of pre- to post-treatment change in PTSD severity, controlling for pre- treatment PTSD severity and relevant psychiatric comorbidities. This project aims to determine whether computational markers of reward processing (Aim 1) and associated neural correlates of reward anticipation (Aim 2) at the onset of EBT can be useful in predicting reduction in PTSD symptoms among trauma-expose... ## Key facts - **NIH application ID:** 10795644 - **Project number:** 5I01CX002456-02 - **Recipient organization:** VA SAN DIEGO HEALTHCARE SYSTEM - **Principal Investigator:** Katia Harle - **Activity code:** I01 (R01, R21, SBIR, etc.) - **Funding institute:** VA - **Fiscal year:** 2024 - **Award amount:** — - **Award type:** 5 - **Project period:** 2023-02-01 → 2027-01-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10795644 ## Citation > US National Institutes of Health, RePORTER application 10795644, Neuro-computational predictors of treatment responsiveness in trauma-exposed Veterans. (5I01CX002456-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10795644. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*