Identifying cognitive markers of late-life suicide

描述(申请人提供)：尽管抑郁症通常先于晚年的自杀行为，但临床医生仍然不能自信地识别最有可能试图自杀或死于自杀的抑郁症老年人。因此，需要更好的预测模型来预测老年人的自杀行为。这个修订的R01(MH085651)应用程序是为了调查晚年自杀行为的特定认知漏洞。我们专注于那些可能导致压力源积累、削弱威慑力并促成结束生命的最终决定的特征。我们的初步数据表明，(1)涉及奖惩加工的特定认知控制方面的缺陷，以及(2)社会认知方面的缺陷，将抑郁的老年自杀未遂者与抑郁的非自杀老年人区分开来，而两组人在全局认知、工作记忆和前瞻性计划方面表现出相似的表现。在这一初步证据的基础上，这位新的研究者R01将在一个足够大的样本中包括关键的认知探测，以测试决策、情感处理、反向学习和社会认知方面的障碍与抑郁老年人的自杀企图具体相关的假设。我们建议使用理论驱动的计算机化评估和传统的认知表现测试来评估100名自杀未遂者、80名非自杀抑郁症患者和60名60岁及以上的非精神控制组受试者。参与者将接受广泛的临床特征，包括他们的自杀行为、精神病理学、心理社会压力源、身体健康、自杀企图可能造成的脑损伤以及药物暴露。三组在人口学特征和医疗疾病负担方面相似，两组抑郁严重程度相似。为了确定尽管情绪状态发生变化，已发现的损害是否会随着时间的推移而持续存在，我们将在基线后四个月重复认知评估(当根据我们的试验数据可以合理地预期实质性的临床改善时)。我们还将前瞻性地探讨认知状态对这一随访期间自杀相关结果的影响。在与我们晚年抑郁症中心的生物统计团队和我们的外部统计顾问的合作下，我们建议使用多元协方差分析来比较不同群体的认知功能，以及判别函数分析，以创建一个紧凑的认知电池，并测试其在正确识别已知风险因素之外的自杀未遂者的有效性。我们将使用混合效应模型来检验不同情绪状态下认知障碍的稳定性。统计分析将考虑可能影响认知的因素：抑郁的严重程度、医疗疾病负担、血清抗胆碱活性以及初步分析确定的其他相关因素。这个项目建立在正在进行的K23项目的基础上，在K23项目中，PI已经显示了招募、评估和纵向跟踪自杀行为发生率高的自杀老人在后续行动中的可行性。该研究项目将在匹兹堡大学与剑桥大学实验心理学系合作进行。 公共卫生相关性：了解与晚年自杀行为相关的认知缺陷及其与其他风险因素的关系，可能有助于推动老年精神健康领域的转化神经科学，识别有自杀风险的老年人，并帮助开发个性化治疗策略，以预防美国自杀率最高的老年人自杀。这项研究得出的用于评估自杀风险的紧凑型认知电池可用于未来的前瞻性研究和临床环境。

DESCRIPTION (provided by applicant): Although depression commonly precedes late-life suicidal behavior, clinicians still cannot confidently identify depressed elderly who are most likely to attempt or die by suicide. Thus, there is a great need for better predictive models regarding suicidal behavior in the elderly. This revised R01 (MH085651) application is to investigate specific cognitive vulnerabilities to late-life suicidal behavior. We focus on features that may cause accumulation of stressors, undermine deterrents, and facilitate the final decision to take one's life. Our preliminary data indicate that deficits in (1) specific aspects of cognitive control that involve reward/punishment processing, and in (2) social cognition distinguish depressed elderly suicide attempters from depressed non- suicidal elderly, while the two groups show similar global cognition, working memory, and forward planning. Building on this preliminary evidence, this new-investigator R01 will include key cognitive probes in a large- enough sample to test hypotheses that impairments in decision-making, affective processing, reversal learning, and social cognition are specifically associated with suicide attempts in depressed elders. We propose to assess 100 suicide attempters, 80 non-suicidal depressed individuals, and 60 non-psychiatric control subjects, aged 60 and older, using theory-driven computerized assessments as well as traditional tests of cognitive performance. Participants will undergo extensive clinical characterization of their suicidal behavior, psychopathology, psychosocial stressors, physical health, possible brain injury from suicide attempts, and medication exposure. The three groups will be similar in demographic characteristics and medical illness burden, and the two depressed groups will have similar severity of depression. To determine whether the identified impairments persist over time despite changes in mood state, we will repeat cognitive assessments four months after baseline (when substantial clinical improvement can reasonably be anticipated based on our pilot data). We will also prospectively explore the effect of cognitive status on suicide-related outcomes during this follow-up period. In collaboration with the biostatistical team of our late-life depression center and our external statistical consultant, we propose to use multivariate analyses of covariance to compare cognitive functions across groups, as well as discriminant function analysis to create a compact cognitive battery and to test its utility for correctly identifying suicide attempters beyond known risk factors. We will use mixed effects models to examine stability of cognitive impairments across mood states. Statistical analysis will account for factors that may affect cognition: severity of depression, medical illness burden, serum anticholinergicity, and other relevant factors identified by preliminary analyses. This project builds upon an ongoing K23, where the PI has shown the feasibility of recruiting, assessing, and longitudinally following suicidal elders with a high rate of suicidal behavior during follow-up. The research project will be conducted at the University of Pittsburgh, in collaboration with the Experimental Psychology Department, University of Cambridge. PUBLIC HEALTH RELEVANCE: Understanding cognitive deficits associated with late-life suicidal behavior and their relationship to other risk factors may help to advance translational neuroscience in geriatric mental health, identify elderly people at risk for suicide, and help to develop individualized treatment strategies in the service of preventing suicide in older people, who have the highest suicide rate in the US. The compact cognitive battery for assessing suicide risk derived from this research can be used in future prospective studies and in clinical settings.