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).

虽然针对 PTSD 的循证治疗 (EBT) 可有效减少与创伤相关的焦虑 大约一半到三分之二的遭受创伤的退伍军人在治疗期间没有完全康复 维持他们的 PTSD 诊断，即对奖励活动的兴趣和参与减少。 普遍存在于遭受创伤的退伍军人中，并且与创伤后应激障碍 (PTSD) 严重程度较高和较差有关 因此，对精神治疗的反应受损可能在其中发挥关键作用。 然而，迄今为止，这种奖励敏感性的程度。 为了检验这一假设，拟议的研究将影响 PTSD 治疗反应。 结合计算模型和事件相关的功能磁共振成像 (fMRI) 进行分析 退伍军人在认知处理治疗（CPT）结束时的奖励处理功能，并测试 这些标记在预测治疗反应方面的有用性，特别是在计算模型中。 与神经影像学相结合，提供了对复杂认知过程的详细机制见解，这可以 我们将比标准行为和神经影像分析更准确地预测临床结果。 利用这种方法来描绘遭受创伤的退伍军人治疗反应的强有力的预测因素。 总共 186 名遭受过创伤的退伍军人将在参加 CPT 后立即招募。 完成完整的临床评估和两项多臂老虎机 (MAB) 任务（在经典和社会条件下，以 在探索性分析中进行比较），其中他们必须在每次试验中从一组选项中进行选择 奖励概率未知，目标是最大化并发大脑活动。 对 93 名退伍军人的子集进行了测量，他们将在接受 fMRI 贝叶斯学习的同时完成任务。 模型将应用于参与者的决策，以得出个人层面的参数，代表 a) 个人的 b）他们的模型对环境中未知奖励率的感知稳定性 基于奖励的期望影响他们的选择，这些神经激活与试验到参数相关。 基于试验模型的奖励预期和相关的预测误差（即预期与预期之间的差异） 观察到的奖励）将被提取，所有参与者都将完成后续的临床和行为评估。 治疗后立即和治疗后 3 个月的计算参数和基于模型的神经。 激活将作为治疗前和治疗后 PTSD 严重程度变化的预测因子进行测试，控制治疗前 该项目旨在确定是否存在治疗 PTSD 严重程度和相关精神合并症。 奖励处理的计算标记（目标 1）以及奖励的相关神经关联 EBT 开始时的预期（目标 2）有助于预测 PTSD 症状的减轻 目标 3 将评估这些计算标记是否适用于遭受创伤的退伍军人。 治疗后 3 个月的治疗后结果的预测。 奖励处理的计算标记及其与快感缺乏和创伤后应激障碍（PTSD）变化的关系 还将探讨严重程度（目标 4）。 并为有恢复不良风险的退伍军人制定治疗计划和复发预防工具 识别这种预测机制也将提供关键的神经和心理目标。 开发更有效、个性化的治疗方法以改善创伤后应激障碍 (PTSD) 的恢复（例如，认知训练 提高奖励敏感性并减少快感缺乏）。