Network Science Methodology for Assessing PTSD Risk

描述（由申请人提供）：本申请旨在引入一种相对较新的研究方法，称为网络科学（NS），以了解创伤后应激障碍复杂和多因素决定的精神病理的风险因素。网络科学已被应用于许多科学领域，以了解最有助于复杂现象出现和持续的变量。NS的方法能够确定一组给定的变量是否发展了所谓的“复杂适应系统（CAS）”的属性。CAS的基本特性包括自组织、自维持和鲁棒性。CAS可以产生于自然现象（如细胞、疾病）或人为现象（如互联网、经济）。一旦CAS出现，这个复杂的变量系统就会对外部挑战具有高度的抵抗力。这一观点影响了许多重要领域的生物医学研究（例如癌症、传染病、自闭症），“网络医学”一词被创造出来，用来描述网络医学在生物医学研究中的应用。这个应用程序汇集了一个具有不同专业领域的团队，非常适合将NS应用于创伤后应激障碍的风险因素研究。本研究集中了以下领域的专家：1)创伤后应激障碍的生物行为危险因素，2)创伤后应激障碍基因组学，3)计算生物学和生物信息学，4)儿童发育，以及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PTSD相关的一系列复杂变量是否可能构成一个复杂的适应系统；以及这种系统的坚固性是否会导致PTSD的治疗难治性。网络科学方法学将应用于1)分析两个令人信服的纵向数据集，这些数据集包含非常适合理解创伤后应激障碍系统特性的信息；2)基于对创伤后应激障碍（及相关疾病）之间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构；以及与这些疾病相关的基因和蛋白质。如果NS揭示了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。本应用程序旨在引入一种相对较新的研究方法，称为网络科学（NS），以了解创伤后应激障碍的复杂和多因素精神病理的风险因素。网络科学已被应用于许多科学领域，以了解最有助于复杂现象出现和持续的变量。NS的方法能够确定一组给定的变量是否发展了所谓的“复杂适应系统（CAS）”的属性。如果NS揭示了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.