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

贝叶斯变量选择方法加速重要心理预测因素和精神病理学神经基础的识别

• 批准号: 10592357
• 负责人: Sierra Bainter
• 金额: $ 13.15万
• 依托单位: UNIVERSITY OF MIAMI CORAL GABLES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-08-15
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10592357
• 关键词: Acceleration; Accounting; Address; Adult; Age; Anxiety; Area; Award; Behavioral; Biological Factors; Brain; Chronic; Clinical; Computer Simulation; Computer software; Data; Data Set; Databases; Development; Disease; Early Intervention; Early identification; Environmental Risk Factor; Etiology; Evaluation; Funding; Goals; Guidelines; Individual; Investigation; Journals; Longevity; Measures; Mental Health; Mental disorders; Mentors; Methodology; Methods; Modeling; National Institute of Mental Health; Neurosciences; Obsessive compulsive behavior; Obsessive-Compulsive Disorder; Onset of illness; Outcome; Personality; Persons; Phenotype; Positioning Attribute; Predictive Value; Prevention; Probability; Psychological Factors; Psychology; Psychopathology; Recommendation; Research; Research Design; Research Personnel; Resources; Risk; Risk Factors; Sampling; Statistical Methods; Stressful Event; Symptoms; Techniques; Temperament; Time; Training; Uncertainty; Universities; Visualization; Work; abuse neglect; biomarker identification; burden of illness; comorbidity; experimental study; negative affect; neural; neuroimaging; novel; open source; psychologic; public database; resilience factor; simulation; stressor; substance use; theories; tool; user-friendly

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.

项目摘要 NIMH试图“在课程的早期就确定具有高预测价值的生物标志物和行为指示剂 为了减轻整体心理负担。 潜在的重要心理，环境和生物学数量，精神健康障碍 是巨大的，一个关键的挑战是缩小最重要的挑战 特别是difficalt是1）神经科学的最新进展开始揭示 心理病理学和2）许多预测因素是自身相关的 控制其他因素后，因素与疾病可靠地相关。 不足以克服这种挑战。 搜索变量选择（SSV）可用于识别具有最大关系的预测变量 给定标准，但是这些方法尚未开发用于心理学，目前仅是 用于专业统计数据。 研究人员使用SSV克服当前的方法论障碍。 使SSV容易获得的应用程序。 评估SSV在各种条件下的工作方式的研究，并为 在此目的的第二个目标中，我将使用SSV来预测强迫症 （OCD）Nathane Kline Institute Rockland样本中的症状，该样本是一个大型公共数据库。 强迫症是一种常见，慢性和衰弱的障碍。 旨在治疗和预防的限制。 对于疾病开始，疾病的发作很长一段时间在症状开始后 该样本包括广泛的理论风险因素，包括潜在的措施 心理局限性脆弱性，大脑联系，兴奋的生活事件和关键合并症。 研究嵌入了培训和指导计划中，该计划在1）病因和 评估心理病理学，2）确定神经基质的神经科学方法 精神病理学和3）贝叶斯变量选择方法。 我为我的培训，时间和资源做出了重大进步，以增加这个iSaltant 问题并确立自己是一名独立的R01资助的研究者。

项目成果

Child eyewitness researchers often bin age: Prevalence of the practice and recommendations for analyzing developmental trends.

• DOI: 10.1037/lhb0000416
• 发表时间: 2020-08
• 期刊: Law and human behavior
• 影响因子: 2.5
• 作者: Bainter SA;Tibbe TD;Goodman ZT;Poole DA
• 通讯作者: Poole DA

Corrigendum to "Perceived neighborhood factors, health behaviors, and related outcomes in the Hispanic Community Health Study/Study of Latinos" [Preventive Medicine 2022 Nov;164:107267. Epub 2022 Sep 20].

• DOI: 10.1016/j.ypmed.2023.107470
• 发表时间: 2023-04
• 期刊: PREVENTIVE MEDICINE
• 影响因子: 5.1
• 作者: Bayly, Jennifer E.;Panigrahi, Asmi;Rodriquez, Erik J.;Gallo, Linda C.;Perreira, Krista M.;Talavera, Gregory A.;Estrella, Mayra L.;Daviglus, Martha L.;Castaneda, Sheila F.;Bainter, Sierra A.;Chambers, Earle C.;Savin, Kimberly L.;Loop, Matthew;Perez-Stable, Eliseo J.
• 通讯作者: Perez-Stable, Eliseo J.

Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology.

• DOI: 10.1007/s11336-023-09914-9
• 发表时间: 2023-09
• 期刊: PSYCHOMETRIKA
• 影响因子: 3
• 作者: Bainter, Sierra A.;McCauley, Thomas G.;Fahmy, Mahmoud M.;Goodman, Zachary T.;Kupis, Lauren B.;Rao, J. Sunil
• 通讯作者: Rao, J. Sunil