Though lifetime exposure to traumatic events is significant, only a minority of individuals develops symptoms of posttraumatic stress disorder (PTSD). Post-trauma alterations in neurocognitive and affective functioning are likely to reflect changes in underlying brain networks that are predictive of PTSD. These constructs are assumed to interact in a highly complex way. The aim of this exploratory study was to apply machine learning models to investigate the contribution of these interactions on PTSD symptom development and identify measures indicative of circuit related dysfunction. N = 94 participants admitted to the emergency room of an inner-city hospital after trauma exposure completed a battery of neurocognitive and emotional tests 1 month after the incident. Different machine learning algorithms were applied to predict PTSD symptom severity and clusters after 3 months based. Overall, model accuracy did not differ between PTSD clusters, though the importance of cognitive and emotional domains demonstrated both key differences and overlap. Alterations in higher-order executive functioning, speed of information processing, and processing of emotionally incongruent cues were the most important predictors. Data-driven approaches are a powerful tool to investigate complex interactions and can enhance the mechanistic understanding of PTSD. The study identifies important relationships between cognitive processing and emotion recognition that may be valuable to predict and understand mechanisms of risk and resilience responses to trauma prospectively.

中文翻译：

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利用机器学习来测试认知处理和情绪识别对创伤暴露后 PTSD 发展的影响。


尽管一生中遭受创伤事件的概率很大，但只有少数人会出现创伤后应激障碍 (PTSD) 症状。创伤后神经认知和情感功能的改变可能反映了可预测 PTSD 的潜在大脑网络的变化。假设这些结构以高度复杂的方式相互作用。这项探索性研究的目的是应用机器学习模型来调查这些相互作用对 PTSD 症状发展的贡献，并确定指示回路相关功能障碍的措施。 N = 94 名参与者在遭受创伤后被送往市中心一家医院的急诊室，并在事件发生后 1 个月完成了一系列神经认知和情绪测试。应用不同的机器学习算法来预测 3 个月后的 PTSD 症状严重程度和集群。总体而言，尽管认知和情感领域的重要性表现出关键差异和重叠，但 PTSD 集群之间的模型准确性没有差异。高阶执行功能、信息处理速度以及情绪不一致线索的处理的改变是最重要的预测因素。数据驱动的方法是研究复杂相互作用的强大工具，可以增强对 PTSD 机制的理解。该研究确定了认知处理和情绪识别之间的重要关系，这对于前瞻性预测和理解创伤风险和复原力反应机制可能有价值。