Network Science Methodology for Assessing PTSD Risk

描述(由申请人提供)：这项申请寻求将一种相对较新的研究方法称为网络科学(NS)，以了解复杂和多决定的创伤后应激障碍精神病理学的风险因素。网络科学已被应用于科学追求的许多领域，以了解对复杂现象的出现和持续做出最大贡献的变量。NS的方法论使得能够确定一组给定的变量是否发展了已被称为“复杂适应系统(CAS)”的特性。CAS的基本属性包括自组织性、自持性和稳健性。CAS可以从自然现象(如细胞、疾病)或人为现象(如互联网、经济)中产生。一旦CAS出现，这个复杂的变量系统就会高度抵抗外部挑战。这一观点影响了许多重要领域(如癌症、传染病、自闭症)的生物医学研究，并创造了术语“网络医学”来描述NS在生物医学研究中的应用。这一应用程序汇集了一支具有不同专业知识领域的团队，非常适合将NS应用于创伤后应激障碍的风险因素研究。这项拟议的研究具有以下领域的专业知识：1)创伤后应激障碍的生物行为风险因素，2)创伤后应激障碍的基因组学，3)计算生物学和生物信息学，4)儿童发展，5)与创伤后应激障碍相关的纵向研究方法。该团队将共同努力，确定与创伤后应激障碍相关的一组复杂变量是否构成一个复杂的适应系统，以及这种系统的强大特性是否导致创伤后应激障碍的治疗难治性。网络科学方法论将应用于1)分析两个引人注目的纵向数据集，其中包含非常适合于理解创伤后应激障碍的系统特性的信息；以及2)基于对创伤后应激障碍(与相关疾病)之间的关系以及与这些疾病相关的基因和蛋白质之间的关系的现有信息的查询，创建创伤后应激障碍的分子网络重建。如果NS揭示了一个与创伤暴露和创伤后应激障碍相关的复杂适应系统，那么通过了解这种系统如何持续或失败，可以为治疗创伤后应激障碍的干预方法提供实质性的信息。这项申请寻求将一种相对较新的研究方法称为网络科学(NS)，以了解复杂和多决定的创伤后应激障碍精神病理学的风险因素。网络科学已被应用于科学追求的许多领域，以了解对复杂现象的出现和持续做出最大贡献的变量。NS的方法论使得能够确定一组给定的变量是否发展了已被称为“复杂适应系统(CAS)”的特性。如果NS揭示了一个与创伤暴露和创伤后应激障碍相关的复杂适应系统，那么通过了解这种系统如何持续或失败，可以为治疗创伤后应激障碍的干预方法提供实质性的信息。

DESCRIPTION (provided by applicant): This application seeks to bring a relatively new research methodology called Network Science (NS) to the understanding of risk factors for the complex and multi-determined psychopathology of PTSD. Network Science has been applied, in many areas of scientific pursuit, to understand the variables that most contribute to the emergence and persistence of complex phenomena. The methodology of NS enables the determination of whether a given set of variables develops the properties of what has been termed a 'Complex Adaptive System (CAS)'. The essential properties of a CAS include self-organization, self- sustenance, and robustness. A CAS can emerge from natural (e.g. a cell, a disease) or human-made (e.g. the internet, an economy) phenomena. Once a CAS emerges, this complex system of variables becomes highly resistant to external challenge. This perspective has influenced biomedical research in a number of important areas (e.g. cancer, infectious disease, autism) and the term 'Network Medicine' has been coined to describe the application of NS to biomedical research. This application brings together a team with diverse areas of expertise ideally suited to the application of NS to risk factor research for PTSD. Expertise in the following areas is featured in this proposed research: 1) bio-behavioral risk factors for PTSD, 2) genomics of PTSD, 3) computational biology and bioinformatics, 4) child development, and 5) longitudinal research methodology related to PTSD. This team will work together to determine if a complex set of variables related to PTSD may constitute a Complex Adaptive System; and whether the robust properties of such a system lead to the treatment refractory nature of PTSD. Network Science methodology will be applied to 1) the analysis of two compelling longitudinal datasets that contain information ideally suited to understanding the systemic properties of PTSD; and 2) the creation of a Molecular Network Reconstruction of PTSD based on queries of available information on the relationship between PTSD (and related disorders); and the genes and proteins associated with these disorders. If NS reveals a Complex Adaptive System related to traumatic exposure and PTSD, intervention approaches to treat PTSD can be substantially informed by understanding how such a system persists or fails. This application seeks to bring a relatively new research methodology called Network Science (NS) to the understanding of risk factors for the complex and multi-determined psychopathology of PTSD. Network Science has been applied, in many areas of scientific pursuit, to understand the variables that most contribute to the emergence and persistence of complex phenomena. The methodology of NS enables the determination of whether a given set of variables develops the properties of what has been termed a 'Complex Adaptive System (CAS)'. If NS reveals a Complex Adaptive System related to traumatic exposure and PTSD, intervention approaches to treat PTSD can be substantially informed by understanding how such a system persists or fails.