A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse

<b>Background:</b> The network approach to mental disorders offers a novel framework for conceptualizing posttraumatic stress disorder (PTSD) as a causal system of interacting symptoms. <b>Objective:</b> In this study, we extended this work by estimating the structure of relations among PTSD symptoms in adults reporting personal histories of childhood sexual abuse (CSA; N = 179). <b>Method:</b> We employed two complementary methods. First, using the graphical LASSO, we computed a sparse, regularized partial correlation network revealing associations (edges) between pairs of PTSD symptoms (nodes). Next, using a Bayesian approach, we computed a directed acyclic graph (DAG) to estimate a directed, potentially causal model of the relations among symptoms. <b>Results:</b> For the first network, we found that physiological reactivity to reminders of trauma, dreams about the trauma, and lost of interest in previously enjoyed activities were highly central nodes. However, stability analyses suggest that these findings were unstable across subsets of our sample. The DAG suggests that becoming physiologically reactive and upset in response to reminders of the trauma may be key drivers of other symptoms in adult survivors of CSA. <b>Conclusions:</b> Our study illustrates the strengths and limitations of these network analytic approaches to PTSD.

**背景：** 精神障碍的网络研究范式为将创伤后应激障碍（Posttraumatic Stress Disorder, PTSD）视为症状交互作用的因果系统提供了全新的理论框架。**目的：** 本研究针对有儿童性虐待（Childhood Sexual Abuse, CSA）史的成年人群（N=179），通过估算PTSD症状间的关联结构拓展了上述研究方向。**方法：** 本研究采用两种互补的分析方法。其一，通过图LASSO（graphical LASSO）构建稀疏正则化偏相关网络，以揭示PTSD症状节点（nodes）间的关联边（edges）；其二，采用贝叶斯方法构建有向无环图（directed acyclic graph, DAG），以估算症状间的有向潜在因果关联模型。**结果：** 针对首个偏相关网络，研究发现创伤线索诱发的生理反应、创伤相关梦境以及既往喜好活动的兴趣减退均为核心节点。不过稳定性分析显示，该结果在样本子集间并不稳健。有向无环图结果表明，面对创伤线索时出现生理反应与情绪烦躁，可能是儿童性虐待成年幸存者其他PTSD症状的关键驱动因素。**结论：** 本研究阐明了此类网络分析方法应用于PTSD研究的优势与局限。