Identifying cognitive markers of late-life suicide

描述（由申请人提供）：尽管抑郁症通常先于晚年自杀行为，临床医生仍然不能自信地识别出最有可能试图自杀或死于自杀的抑郁老年人。因此，对于老年人自杀行为的预测模型是非常必要的。本修订后的R01 （MH085651）应用程序旨在调查老年自杀行为的特定认知脆弱性。我们关注的特征可能会导致压力源的积累，破坏威慑，并促进最终决定自杀。我们的初步数据表明：(1)涉及奖励/惩罚处理的认知控制的特定方面的缺陷，以及(2)社会认知的缺陷区分了抑郁的老年自杀未遂者和抑郁的老年非自杀未遂者，而这两组表现出相似的全局认知、工作记忆和前瞻性计划。在这个初步证据的基础上，这个新的研究者R01将在一个足够大的样本中包括关键的认知探测，以检验决策、情感处理、逆转学习和社会认知方面的障碍与抑郁症老年人自杀企图具体相关的假设。我们建议对100名自杀未遂者、80名非自杀性抑郁个体和60名年龄在60岁及以上的非精神病学对照组进行评估，采用理论驱动的计算机评估和传统的认知表现测试。参与者将接受有关其自杀行为、精神病理、心理社会压力源、身体健康、自杀企图可能造成的脑损伤和药物暴露的广泛临床特征。这三个群体在人口统计学特征和医疗疾病负担方面相似，两个抑郁群体的抑郁严重程度相似。为了确定尽管情绪状态发生变化，但已识别的损伤是否会随着时间的推移而持续，我们将在基线后四个月重复认知评估（当根据我们的试点数据可以合理地预期实质性的临床改善时）。在随访期间，我们还将前瞻性地探讨认知状态对自杀相关结果的影响。在与我们的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中，我们建议使用协方差的多变量分析来比较不同群体的认知功能，以及判别函数分析来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企图者方面的效用。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：抑郁症的严重程度、医疗疾病负担、血清抗胆碱能和其他初步分析确定的相关因素。该项目建立在正在进行的K23的基础上，其中PI已经显示了招募，评估和纵向跟踪在随访期间自杀行为率高的自杀长者的可行性。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。

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.