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

优化抑郁老年人的安慰剂效应：通过计算机认知训练提高处理速度和执行功能

• 批准号: 10194015
• 负责人: JOEL R. SNEED
• 金额: $ 40.67万
• 依托单位: QUEENS COLLEGE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10194015
• 关键词: Address; Adult; Age; Amygdaloid structure; Antidepressive Agents; Behavior; Belief; Brain region; Cerebrovascular Disorders; Chronic; Clinical assessments; Complex; Depressed mood; Development; Disease remission; Elderly; Escitalopram; Executive Dysfunction; Exercise; Expectancy; Failure; Functional disorder; Goals; Impairment; Intervention; Investigational Therapies; Knowledge; Laboratories; Longevity; MRI Scans; Magnetic Resonance Imaging; Major Depressive Disorder; Mediating; Mediator of activation protein; Mental Depression; Mental Health; Modeling; Morbidity - disease rate; National Institute of Mental Health; Neurocognitive; Neurocognitive Deficit; Neuropsychology; Outcome; Participant; Pathway interactions; Patients; Pharmaceutical Preparations; Placebo Effect; Placebos; Population; Prefrontal Cortex; Prevention; Public Health; Quality of life; Randomized; Recurrence; Research; Residual state; Rest; Role; Selective Serotonin Reuptake Inhibitor; Signal Transduction; Strategic Planning; Structure; Symptoms; Technology; Testing; Training; Translations; Treatment outcome; Ventral Striatum; White Matter Hyperintensity; active control; base; cognitive control; cognitive function; cognitive process; cognitive reappraisal; cognitive training; computerized; depressive symptoms; design; digital; disability; executive function; expectation; geriatric depression; improved; indexing; mortality; neural circuit; neuromechanism; novel; novel therapeutics; pill; processing speed; recruit; response; therapy resistant; treatment response

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.

项目摘要：重度抑郁症 (MDD) 是导致残疾、发病和死亡的主要原因 整个生命周期的死亡率，并在晚年造成特别严重的公共卫生问题。 (LLD) 复发率很高，可以转为慢性，并且通常很难通过抗抑郁治疗来治疗。 由于存在神经认知因素（包括处理速度慢），因此对该人群无效 （PS）、执行功能障碍（ED）和干扰治疗的脑血管疾病（CVD）。 因此，我们制定干预措施来解决抗抑郁药物无反应的问题并显着改善 我们最近确定，影响数百万弱势老年人生活质量的一个重要原因是 该人群的反应受到预期效应的损害，而预期效应又因反应速度缓慢而受到损害 因此，我们认为，PS 缺陷的老年人对抗抑郁药物无反应是造成的。 预期失败，并且在抗抑郁治疗之前针对 PS 缺陷将恢复能力 形成期望，从而改善抗抑郁治疗反应。 PS 是计算机化认知训练 (CCT)，即针对、训练和强化特定认知的练习 使用结构化演练和重复练习来测试我们的预期处理速度。 模型中，将随机招募 100 名 60 岁及以上患有 PS 缺陷的抑郁症成年人。 CCT 或对照（Solitaire）为期 4 周，每周 7 天，每天 25 分钟。 在这四个星期结束时，患者将被随机分配到高期望组和低期望组 被分配到低期望条件的患者将被告知他们将接受其中一种治疗条件。 安慰剂或依他普仑，而事实上他们将接受依他普仑八周的治疗。 高期望状况将被告知他们将接受八周的艾司西酞普兰治疗。 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 4 周进行。 第 4 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 是否调节 CCT 和预期之间的关系，以及 2) 比较终点 抑郁评分作为 CCT 和预期条件的函数。 该提案中采取的方法跨越了几个研究主题（预防和转化）并解决了 NIMH 的心理战略计划中详细阐述了许多挑战（数字技术和神经回路） 与 NIMH 的目标一致，它还制定了调整现有战略的战略。 优化结果的干预措施并阐明 LLD 抗抑郁治疗的机制 被恢复。