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

项目摘要 NIMH寻求在课程的早期识别具有高预测价值的生物标记物和行为指标 尽可能减少疾病发展的负担“，以减轻精神疾病的总体负担。然而， 精神健康障碍的潜在重要心理、环境和生物因素的数量 是巨大的，一个关键的挑战是缩小到最重要的预测混乱的因素。这项挑战是 变得特别困难，因为神经科学的最新进展开始揭示 精神病理学，以及2)许多预测因素本身是相关的，这使得很难区分哪一个 在控制了其他因素后，这些因素与疾病有可靠的联系。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)，可用于识别具有最稳健关系的预测值 然而，这些方法并不是为心理学而开发的，目前仅限于 可供专业统计员使用。该项目的目标是制定能够实现心理健康的指导方针。 研究人员使用SSVS来克服目前的方法障碍。我还将开发用户友好的在线 使SSVS更容易获得的应用程序。对于这项研究的第一个目的，我将使用计算机模拟 研究以评估SSVS在一系列条件下的工作方式，并开发指南和软件 研究人员可以使用。在这项研究的第二个目标中，我将应用SSVS来预测强迫症 内森·克莱恩研究所罗克兰样本中的强迫症症状，这是一个大型的公开数据库。 强迫症是一种常见的、慢性的、令人衰弱的疾病。关于强迫症的风险仍不清楚，这 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 因为疾病的发生在很大程度上依赖于在症状出现很长一段时间后对个人的评估。中的预测值 本样本包括一系列从理论上得出的风险因素，包括对潜在风险的衡量。 心理脆弱性、大脑连通性、应激性生活事件和关键的共病。这项建议 研究被嵌入培训和指导计划，该计划将提供1)病因学和 精神病理学评估，2)神经科学方法确定神经底物 3)贝叶斯变量选择方法。这项K01指导研究奖将提供 培训、时间和资源使我在解决这一重要问题方面取得实质性进展 问题，并将自己确立为一名独立的、由R01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.