Bayesian Variable Selection Methods to Accelerate Identification of Important Psychological Predictors and Neural Substrates of Psychopathology

项目摘要 NIMH寻求“在早期阶段识别具有高预测价值的生物标志物和行为指标 为了减轻精神疾病的总体负担，我们必须尽可能减少疾病的发展。但 许多潜在的重要心理、环境和生物因素导致精神健康障碍 是巨大的，一个关键的挑战是缩小到最重要的预测障碍。这一挑战 特别困难的是：1）神经科学的最新进展开始揭示了 精神病理学，2）许多预测因素本身是相关的，很难理清哪些因素 在控制其他因素后，这些因素与疾病可靠相关。目前使用的统计方法有 不足以克服这一挑战。强大的贝叶斯变量选择方法，称为随机 搜索变量选择（SSVS），可以用来识别预测与最强大的关系， 然而，这些方法还没有被开发用于心理学，目前仅用于 提供给专业统计人员。该项目的目标是制定指导方针， 研究人员使用SSVS来克服目前的方法障碍。我也会在网上开发人性化的 应用程序，使SSVS易于使用。对于本研究的第一个目标，我将使用计算机模拟 研究评估SSVS如何在一系列条件下工作，并制定指导方针和软件， 研究人员使用。在本研究的第二个目的中，我将应用SSVS来预测强迫症 (OCD)Nathan Kline研究所罗克兰样本中的症状，这是一个大型的公开数据库。 强迫症是一种常见的、慢性的、使人衰弱的疾病。许多关于强迫症的风险仍然未知， 限制了旨在治疗和预防的努力。以前的研究，以确定潜在的风险因素和触发因素 对于疾病的发作，在很大程度上依赖于症状开始后很久对个体的评估。中的预测器 这个样本包括广泛的理论推导的风险因素，包括潜在的措施， 心理脆弱性、大脑连接性、压力性生活事件和主要合并症。这一拟议 研究是嵌入在一个培训和指导计划，将提供培训，1）病因学， 精神病理学评估，2）神经科学方法，以确定神经基板的 精神病理学; 3）贝叶斯变量选择方法。K 01指导研究奖将提供 培训、时间和资源，使我能够在解决这一重要问题方面取得实质性进展。 问题，并建立自己作为一个独立的，R 01资助的调查员。

Project Summary NIMH seeks to “identify biomarkers and behavioral indicators with high predictive value, as early in the course of illness development as possible”, in order to reduce the overall burden of mental illness. However, the number of potentially important psychological, environmental, and biological factors of mental health disorders is vast, and a key challenge is to narrow down to the most important predictors of disorder. This challenge is made especially difficult as 1) recent advances in neuroscience begin to reveal neural substrates of psychopathology, and 2) many predictors are themselves correlated, making it difficult to disentangle which factors are reliably related to disease, after controlling for other factors. Currently used statistical methods are inadequate to overcome this challenge. Powerful Bayesian variable selection methods, called stochastic search variable selection (SSVS), can be used to identify predictors with the most robust relationships for a given criterion, however these methods have not been developed for use in psychology and are currently only available to specialized statisticians. The goal of this project is to develop guidelines to enable mental health researchers to use SSVS to overcome current methodological barriers. I will also develop user-friendly online applications to make SSVS easily available. For the first Aim of this study I will use computer simulation studies to evaluate how SSVS works across a range of conditions and develop guidelines and software for researchers to use. In the second Aim of this study I will apply SSVS to predict obsessive compulsive disorder (OCD) symptoms in the Nathan Kline Institute Rockland sample, which is a large, publicly available database. OCD is a common, chronic, and debilitating disorder. Much regarding risk for OCD remains unknown, which limits efforts aimed at treatment and prevention. Previous research to identify potential risk factors and triggers for illness onset has relied heavily on evaluation of individuals long after symptoms began. The predictors in this sample include a wide range of theoretically-derived risk factors, including measures of potential psychological vulnerabilities, brain connectivity, stressful life events, and key comorbidities. This proposed research is embedded in a training and mentoring plan that will provide training in 1) the etiology and assessment of psychopathology, 2) neuroscience approaches to determine neural substrates of psychopathology, and 3) Bayesian variable selection methods. This K01 mentored research award will provide the training, time and resources for me to make substantial advances towards addressing this important problem and establish myself as an independent, R01-funded investigator.