Posttraumatic Stress Disorder (PTSD) is a prevalent and debilitating condition with complex and variable presentation. While PTSD symptom domains (intrusion, avoidance, cognition/mood, and arousal/reactivity) correlate highly, the relative importance of these symptom subsets often differs across patients. In this study, we used machine learning to derive how PTSD symptom subsets differ based upon brain functional connectivity. We acquired resting-state magnetic resonance imaging in a sample (N = 50) of PTSD patients and characterized clinical features using the PTSD Checklist for DSM-5 (PCL-5). We compared connectivity among 100 cortical and subcortical regions within the default mode, salience, executive, and affective networks. We then used principal component analysis and least-angle regression (LARS) to identify relationships between symptom domain severity and brain networks. We found connectivity predicted PTSD symptom profiles. The goodness of fit (R²) for total PCL-5 score was 0.29 and the R² for intrusion, avoidance, cognition/mood, and arousal/reactivity symptoms was 0.33, 0.23, −0.01, and 0.06, respectively. The model performed significantly better than chance in predicting total PCL-5 score (p = 0.030) as well as intrusion and avoidance scores (p = 0.002 and p = 0.034). It was not able to predict cognition and arousal scores (p = 0.412 and p = 0.164). While this work requires replication, these findings demonstrate that this computational approach can directly link PTSD symptom domains with neural network connectivity patterns. This line of research provides an important step toward data-driven diagnostic assessments in PTSD, and the use of computational methods to identify individual patterns of network pathology that can be leveraged toward individualized treatment.

创伤后应激障碍（PTSD）是一种普遍且令人衰弱的疾病，表现复杂而多变。虽然PTSD症状域（侵入，避免，认知/情绪和唤醒/反应性）高度相关，但是这些症状亚组的相对重要性在不同患者之间通常有所不同。在这项研究中，我们使用机器学习来得出PTSD症状子集如何基于大脑功能连接而不同。我们在样品中获得了静态磁共振成像（N = 50）的PTSD患者，并使用DSM-5的PTSD检查表（PCL-5）表征临床特征。我们比较了默认模式，显着性，执行力和情感网络中100个皮质和皮质下区域之间的连通性。然后，我们使用主成分分析和最小角度回归（LARS）来确定症状域严重程度与脑网络之间的关系。我们发现连接预测的PTSD症状特征。总PCL-5得分的拟合优度（R ²）为0.29，侵入，避免，认知/情绪和唤醒/反应性症状的R ^2分别为0.33、0.23，-0.01和0.06。该模型在预测PCL-5总得分方面的表现明显好于偶然性（p = 0.030）以及入侵和躲避得分（p  = 0.002和p  = 0.034）。它无法预测认知和唤醒得分（p  = 0.412和p  = 0.164）。尽管这项工作需要复制，但这些发现表明，这种计算方法可以将PTSD症状域与神经网络连接模式直接链接起来。这一研究领域为在PTSD中以数据为依据的诊断评估迈出了重要的一步，并使用计算方法来识别可用于个体化治疗的网络病理学的各个模式。