Optimizing placebo effects in depressed older adults: Enhancing processing speed and executive functioning with computerized cognitive training

项目摘要：严重抑郁障碍(MDD)是导致残疾、发病率和 在整个生命周期内的死亡率，并在晚年构成一个特别严重的公共卫生问题。晚年抑郁症 (LLD)是高度复发的，可以变成慢性的，而且通常很难治疗。抗抑郁药物治疗通常是 在这一人群中无效，因为存在神经认知因素，包括处理速度慢 (PS)、执行功能障碍(ED)和脑血管疾病(CVD)干扰治疗。这是至关重要的， 因此，我们开发了解决抗抑郁药无反应的干预措施，并显著改善了 数以百万计的弱势老年人的生活质量。我们最近确定了一个重要的非 这一人群的反应是预期效应受损，而预期效应又因速度缓慢而受到损害 正在处理。因此，我们提出，在PS缺陷的老年人中，抗抑郁药无反应是导致 由于预期失败，在抗抑郁药物治疗之前靶向PS缺陷将恢复 形成预期，从而提高抗抑郁药物的治疗反应。改进的极佳人选 PS是计算机化的认知训练(CCT)，即针对、训练和加强特定认知的练习 使用结构化演练和反复练习的流程。测试我们的预期处理速度 模型，将招募100名60岁及以上有PS缺陷的抑郁症成年人。参与者将被随机分配 CCT或对照组(纸牌)，为期4周。这两种条件都将每天训练25分钟，每周7天。 在这四周的周期结束时，患者将被随机分配到高期望值和低期望值组。 治疗条件。被分配到低预期状况的患者将被告知他们将接受 安慰剂或依西他普兰，而实际上他们将接受8周的埃西他普兰治疗。患者被分配到 高期望值患者将被告知将接受8周的依西他普兰治疗。神经心理学 评估将在基线以及第4周和第12周进行，而MRI扫描将在基线和 第四周。临床评估将在整个研究过程中每两周进行一次。本研究的目标是：1) 确定PS是否在CCT和预期之间的关系中起中介作用，以及2)比较终点 抑郁评分作为CCT和预期条件的函数。新的实验疗法 本提案采用的方法跨越了几个研究主题(预防和翻译)，并解决了 在NIMH的心智战略计划中阐述的许多挑战(数字技术和神经电路) 22世纪的健康研究。与NIMH目标一致，它还制定了定制现有 干预措施以优化结果并阐明LLD抗抑郁治疗的机制 会恢复的。

PROJECT SUMMARY: Major Depressive Disorder (MDD) is a leading cause of disability, morbidity, and mortality across the lifespan and poses a particularly severe public health problem in late life. Late-life depression (LLD) is highly recurrent, can become chronic, and is often difficult to treat. Antidepressant treatment is often ineffective in this population because of the presence of neurocognitive factors including slow processing speed (PS), executive dysfunction (ED), and cerebrovascular disease (CVD) that interfere with treatment. It is crucial, therefore, that we develop interventions that address antidepressant non-response and dramatically improve the quality of life of millions of vulnerable older adults. We recently determined that an important cause of non- response in this population is impaired expectancy effects, which in turn are compromised by slow speed of processing. We propose, therefore, that antidepressant non-response in older adults with PS deficits is caused by expectancy failure and that targeting PS deficits prior to antidepressant treatment will restore the capacity to form expectations thereby improving antidepressant treatment response. An excellent candidate for improving PS is computerized cognitive training (CCT), i.e., exercises that target, train, and strengthen specific cognitive processes with the use of structured drills and repeated practice. To test our expectancy-processing speed model, 100 depressed adults age 60 and over with PS deficits will be recruited. Participants will be randomized to either CCT or control (Solitaire) for 4 weeks. Both conditions will train for 25 minutes per day, 7 days per week. At the conclusion of this four-week period, patients will be randomly assigned to high versus low expectancy treatment conditions. Patients assigned to the low expectancy condition will be told they will receive either placebo or escitalopram when in fact they will receive escitalopram for eight weeks. Patients assigned to the high expectancy condition will be told they will receive escitalopram for eight weeks. Neuropsychological assessment will occur at baseline and weeks 4 and 12 whereas MRI scans will be conducted at baseline and week 4. Clinical assessments will be conducted biweekly throughout the study. The goals of this study are to 1) To determine whether PS mediates the relationship between CCT and expectancy, and 2) To compare endpoint depression scores as a function of CCT and expectancy conditions. The novel experimental therapeutics approach taken in this proposal cuts across several research themes (prevention and translation) and addresses many of the challenges (digital technology and neural circuits) elaborated in NIMH’s Strategic Plan for mental health research in the 22st century. Consistent with NIMH goals, it also develops strategies for tailoring existing interventions to optimize outcomes and elucidates the mechanism by which antidepressant treatment in LLD can be restored.