Augsburger A, et al. - As posttrauma alterations in neurocognitive and affective functioning probably indicate changes in underlying brain networks that are predictive of posttraumatic stress disorder (PTSD). These constructs are thought to interact in a highly complex way. Via performing this exploratory study, researchers applied machine learning models to determine the contribution of these interactions on PTSD symptom development and ascertain the measures indicative of circuit related dysfunction. A battery of neurocognitive and emotional tests were completed by 94 participants admitted to the emergency room of an inner-city hospital after trauma exposure 1 month after the incident. For determining PTSD symptom severity and clusters after 3 months based, they applied different machine learning algorithms. They identified data-driven approaches as a robust tool to determine complex interactions; these approaches can improve the mechanistic understanding of PTSD. Important correlations were observed between cognitive processing and emotion recognition that may be relevant to predict and understand mechanisms of risk and resilience responses to trauma prospectively.