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).

虽然创伤后应激障碍的循证治疗（EBT）在减少创伤相关焦虑方面是有效的， 症状，大约一半到三分之二的创伤暴露退伍军人在治疗期间没有完全恢复， 维持创伤后应激障碍诊断快感缺失，即，减少对奖励活动的兴趣和参与， 在创伤暴露的退伍军人中普遍存在，并与较高的PTSD严重程度和较差的 精神治疗的反应。因此，奖励敏感性受损可能在以下方面发挥关键作用： 退伍军人的治疗反应。然而，到目前为止，这种改变奖励敏感性的程度 对PTSD治疗反应的影响尚未得到验证。为了验证这一假设，拟议的研究将 将联合收割机计算建模和事件相关功能磁共振成像（fMRI）结合起来， 奖励加工功能在退伍军人在认知加工疗法（CPT）结束，并测试 这些标记物在预测治疗反应性中的有用性。计算建模，特别是在 与神经影像学相结合，为复杂的认知过程提供了详细的机械见解， 比标准的行为和神经影像学分析更准确地预测临床结果。我们将 利用这种方法来描述创伤暴露退伍军人治疗反应的可靠预测因素。 共有186名创伤暴露的退伍军人将在CPT注册后立即招募。他们将 完成完整的临床评估和两个多臂强盗（MAB）任务（在经典和社会条件下， 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，目标是最大化总奖励。同时发生的大脑活动 在93名退伍军人的子集中进行测量，这些退伍军人将在接受功能磁共振成像时完成任务。贝叶斯学习 模型将应用于参与者的决定，以获得代表a）个人 环境中未知奖励率的感知稳定性和B）他们的模型- 基于回报的期望会影响他们的选择。神经激活与试验相关参数- 基于试验模型的奖励期望和相关的预测误差（即，预期与 赏析：赏析：赏析。所有参与者将完成后续临床和行为评估 治疗后立即和治疗后3个月。计算参数和基于模型的神经网络 激活将作为治疗前至治疗后PTSD严重程度变化的预测因子进行测试，控制治疗前 治疗PTSD严重程度和相关精神病合并症。该项目旨在确定是否 奖励处理的计算标记（目标1）和相关的奖励神经相关物 EBT发作时的预期（目标2）可用于预测PTSD症状的减轻 创伤暴露的退伍军人中目标3将评估这种计算标记是否 预测治疗后3个月的治疗后结果。治疗相关变化 奖赏加工的计算标记及其与快感缺失和创伤后应激障碍改变的关系 还将探讨严重性（目标4）。这项研究的结果将有助于确定未满足的治疗需求 在退伍军人和制定治疗计划和复发预防工具的退伍军人在风险恢复不良 创伤后应激障碍识别这种预测机制也将提供关键的神经和心理目标 用于开发更有效的个性化治疗以改善PTSD的恢复（例如，认知训练， 提高奖赏敏感性和减少快感缺失）。