Statistical Models of Suicidal Behavior and Brain Biology Using Large Data Sets

使用大数据集的自杀行为和脑生物学的统计模型

• 批准号: 9490063
• 负责人: TODD OGDEN
• 金额: $ 16.32万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9490063
• 关键词: Address; Adopted; Algorithms; Big Data; Biological; Biological Factors; Biology; Brain; Brain imaging; Categories; Complex; DNA; DNA Microarray Chip; Data; Data Analyses; Data Set; Depressed mood; Diagnosis; Diffusion Magnetic Resonance Imaging; Dimensions; Elements; Ensure; Epigenetic Process; Event; Frequencies; Functional Magnetic Resonance Imaging; Future; Gene Expression; Genetic; Genotype; Goals; Heart; Image; Knowledge; Light; MRI Scans; Machine Learning; Magnetic Resonance Spectroscopy; Measures; Mental Depression; Mental disorders; Methodology; Methods; Methylation; Modeling; Neurobiology; Outcome; Patients; Performance; Positron-Emission Tomography; Procedures; Process; Research; Risk; Scanning; Series; Serotonergic System; Severities; Statistical Methods; Statistical Models; Structure; Suicide; Suicide attempt; Survival Analysis; Techniques; Time; Training; Translating; Validation; base; contextual factors; data modeling; depressed patient; genome-wide; high dimensionality; high risk; improved; indexing; insight; interest; model development; neglect; outcome prediction; predicting response; suicidal behavior; suicide attempter; tool; trait; validation studies

SUMMARY-PROJECT 6 The primary objective of the Conte Center is to better understand the causes of suicidal behavior and, ultimately, to translate that knowledge into tools that have the potential for detecting patients at higher risk for more lethal suicide attempts. Towards that aim, a great deal of valuable data is being collected. This project is primarily concerned with very high-dimensional data, including brain imaging data, DNA genetic and epigenetic data, and gene expression data. In particular, we are interested in using such data as predictors in statistical models. Our ultimate goal is to build and implement models that can be used to distinguish depressed suicide attempters from other depressed patients. The greatest hurdle is dealing with very high dimensional data, a situation that classical statistical modeling techniques are not equipped to handle. Our approach to modeling with such high-dimensional data seeks to achieve three primary goals: (1) to provide good predictive accuracy; (2) to build interpretable models that can give meaningful insight into the relationship between the various predictors and the outcome variable; (3) to ensure stability of the models through validation studies. To accomplish these goals our approach will incorporate a regularization component in the model selection and fitting process by applying a penalty to model complexity. In applications, the amount of penalization will be determined by cross-validation or another related technique. In both model development and application, we will consider a wide range of outcome variables including continuous or nearly continuous (e.g., severity of depression, aggressive traits), ordinal (e.g., lethality of suicide attempts), categorical (e.g., diagnosis), and binary (yes/no, e.g., suicide attempt), as well as time-to-event data (e.g., time to a suicide attempt), which will draw on survival analysis techniques. This project focuses specifically on situations in which the number of possible predictors is in the tens of thousands or more. Examples of existing data and data to be gathered as part of the Conte Center include brain imaging data (positron emission tomography (PET) images of the serotonin system, diffusion tensor imaging (DTI) scans, functional magnetic resonance imaging (fMRI) scans, magnetic resonance spectroscopy (MRS) scans), genome-wide DNA genotyping and methylation data, and gene expression data.

摘要项目6 孔戴中心的主要目的是更好地了解自杀行为的原因以及 最终，将这些知识转化为具有检测患者风险更高的患者的工具 更多的致命自杀企图。为了实现这一目标，正在收集大量有价值的数据。这个项目是 主要涉及非常高维数据，包括大脑成像数据，DNA遗传和表观遗传学 数据和基因表达数据。特别是，我们有兴趣使用此类数据作为统计的预测因子 型号。我们的最终目标是建立和实施可用于区分沮丧自杀的模型 来自其他抑郁症患者的示威者。 最大的障碍是处理非常高维数据，这种情况是经典的统计建模 技术无法处理。我们使用如此高维数据建模的方法试图 实现三个主要目标：（1）提供良好的预测准确性； （2）建立可以解释的模型 对各种预测因子与结果变量之间的关系有意义的见解； （3）到 通过验证研究确保模型的稳定性。为了实现这些目标，我们的方法将 通过对模型选择和拟合过程中纳入正规化组件，通过对 模型复杂性。在申请中，惩罚量将由交叉验证或其他罚款确定 相关技术。在模型开发和应用中，我们将考虑广泛的结果 变量，包括连续或几乎连续的变量（例如，抑郁的严重程度，侵略性特征），序数 （例如，自杀企图的致死性），分类（例如诊断）和二进制（是/否，例如自杀企图） 以及事件的时间数据（例如，自杀尝试的时间），这将利用生存分析技术。 该项目专门针对可能的预测因子数量的情况 成千上万。作为孔戴中心的一部分收集的现有数据和数据的示例包括 脑成像数据（正电子发射断层扫描（PET）5-羟色胺系统的图像，扩散张量 成像（DTI）扫描，功能磁共振成像（fMRI）扫描，磁共振光谱 （MRS）扫描），全基因组DNA基因分型和甲基化数据以及基因表达数据。