Neuro-computational predictors of treatment responsiveness in trauma-exposed Veterans.

遭受创伤的退伍军人治疗反应的神经计算预测因子。

• 批准号: 10580396
• 负责人: Katia Harle
• 金额: --
• 依托单位: VA SAN DIEGO HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10580396
• 关键词: Accounting; Address; Affect; Aftercare; Anhedonia; Anterior; Anxiety; Bayesian Analysis; Bayesian Modeling; Bayesian Prediction; Bayesian learning; Behavior assessment; Behavioral; Belief; Biological Assay; Brain; Caring; Clinical; Clinical assessments; Cognition; Cognitive; Complex; Computer Models; Diagnosis; Diagnostic; Dorsal; Effectiveness; Enrollment; Environment; Event; Evidence based treatment; Functional Magnetic Resonance Imaging; Goals; Impairment; Individual; Learning; Link; Measures; Medial; Mental Depression; Modeling; Outcome; Outcome Study; Participant; Perception; Play; Policies; Post-Traumatic Stress Disorders; Prediction of Response to Therapy; Prefrontal Cortex; Preventive; Probability; Process; Psychiatric therapeutic procedure; Psychiatry; Recovery; Research; Rewards; Risk; Risk Factors; Role; Severities; Signal Transduction; Social Conditions; Substance Addiction; Symptoms; Testing; Time; Trauma; Treatment outcome; Triage; Ventral Striatum; Veterans; anxiety symptoms; arm; cingulate cortex; clinical outcome assessment; clinical predictors; cognitive process; cognitive training; expectation; experience; flexibility; follow-up; improved; insight; interest; neural; neural correlate; neuroimaging; novel; personalized medicine; predict clinical outcome; predictive marker; predictive tools; psychiatric comorbidity; psychologic; recruit; relapse prevention; response; reward anticipation; reward processing; substance use; tool; trauma exposure; treatment planning; treatment response

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-exposed Veterans. Aim 3 will assess whether such computational markers are predictive of post-treatment outcomes 3 months after treatment. Treatment-related change in computational markers of reward processing and their relationship to change in anhedonia and PTSD severity will also be explored (Aim 4). The outcomes of this study will help to identify unmet treatment needs in Veterans and develop treatment planning and relapse prevention tools for Veterans at risk for poor recovery from PTSD. Identifying such predictive mechanisms will also provide critical neural and psychological targets for developing more effective, personalized treatments to improve PTSD recovery (e.g., cognitive training to boost reward sensitivity and decrease anhedonia).

而创伤后应激障碍的循证治疗（EBTs）在减少创伤相关焦虑方面是有效的