Network Psychiatry: Using Network Science to Advance Our Understanding of Post-Bereavement Psychopathology

摘要 丧失亲人是一种强大而无处不在的压力源。1000多万人经历了亲人的死亡 在美国每年一例，增加了许多精神疾病的风险，包括复杂的 悲伤。尽管这些疾病带来了巨大的负担，但我们对它们的性质和病因知之甚少 因此，我们预测、预防和治疗丧亲后精神病的能力有限。 最近，我和我的同事们提出，与丧亲相关的精神障碍，如复杂的悲痛， 最好理解为由相互强化的症状组成的复杂系统。在两项研究中，我们使用了 网络科学研究复杂的悲伤症状网络的结构。这些研究使我们能够 确定哪些症状是复杂的悲痛网络最核心的，并提供了一些最早的 有证据表明，了解网络拓扑可以帮助我们理解复杂的悲伤过程。 我这个职业发展奖的核心目标是成为一名网络科学专家，并利用 网络科学的工具，将复杂的悲伤作为一个复杂的心理生物系统来研究。建议数 研究性学习将在我们之前研究的基础上，通过四个关键创新。首先，我们将使用密集的 用于估计每个患者的个体内部网络结构的时间序列数据，提供有关 这个人体内独特的心理生物过程。第二，我们将重点分析 符合丧亲相关研究领域标准的复杂悲痛综合征的要素 (RDoC)构造，将我们的分析与更广泛的工作联系起来，以了解 精神变态学。第三，我们将把生物分析单位纳入我们个人内部的网络中，并 研究生理反应的个体差异是如何导致复杂的悲伤综合症的。 第四，我们将使用个体内部的网络参数来预测复杂悲伤随着时间的推移的过程。 这项研究所采用的方法是创新的，因为它采用了一个新的概念框架 理解和研究精神障碍，植根于快速增长的多学科领域 网络科学。这是有意义的，因为它将阐明患者特有的心理生物学机制 导致与丧亲有关的精神障碍的发展和发病。此外，它还将推出 精神病学领域的新工具，将使我们能够更好地预测、预防和治疗精神病 患者个体的水平，使我们更接近精神病学的精准医学方法的目标。

ABSTRACT Bereavement is a potent and ubiquitous stressor. More than 10 million individuals experience the death a loved one each year in the United States, increasing risk for numerous psychiatric disorders, including complicated grief. Despite the substantial burden conferred by these disorders, we know little about their nature and etiology and, consequently, are limited in our ability to predict, prevent, and treat post-bereavement psychopathology. Recently, my colleagues and I proposed that bereavement-related mental disorders, such as complicated grief, are best understood as complex systems of mutually reinforcing symptoms. In two studies, we used the tools of network science to study the structure of the complicated grief symptom network. These studies allowed us to identify which symptoms are most central to the complicated grief network and provided some of the earliest evidence that understanding network topology can inform our understanding of the course of complicated grief. My core aim for this career development award is to become an expert in network science and to use the tools of network science to study complicated grief as a complex psychobiological system. The proposed research study will build on our previous research through four critical innovations. First, we will use intensive time-series data to estimate the intra-individual network structure for each patient, providing information about the unique psychobiological processes operating within that individual. Second, we will focus our analyses on the elements of the complicated grief syndrome that align with bereavement-related Research Domain Criteria (RDoC) constructs, linking our analyses to the broader effort to understand these individual components of psychopathology. Third, we will incorporate biological units of analysis into our intra-individual network and study how individual differences in physiological reactivity contribute to the complicated grief syndrome. Fourth, we will use intra-individual network parameters to predict the course of complicated grief over time. The approach taken in this study is innovative because it adopts a novel conceptual framework for understanding and studying psychiatric disorders that is rooted in the rapidly growing multi-disciplinary field of network science. It is significant because it will elucidate the patient-specific psychobiological mechanisms contributing to the development and onset of bereavement-related mental disorders. Moreover, it will introduce new tools into the field of psychiatry that will allow us to better predict, prevent, and treat psychopathology at the level of the individual patient, moving us closer to the aim of a precision medicine approach to psychiatry.