Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure

Abstract

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

Distinct cortical thickness correlates of early life trauma exposure and posttraumatic stress disorder are shared among adolescent and adult females with interpersonal violence exposure

“…Similarly, the AG, located in the inferior parietal lobe, is implicated as a major hub for multisensory integration, attention, and information processing [50]. Importantly, the parietal cortex also modulates emotional reappraisal [51] and emotion perception across age groups [52], which can be impaired in individuals with PTSD [53,54]. Consistent with evidence suggesting that adults with PTSD show impairments in verbal learning, sustained attention, and working memory [55], it is possible that disruptions in the specialization of frontoparietal network activity are reflected in increased CT of parietal regions and affect cognitive processing and emotional regulation in individuals with PTSD.…”

A deep belief network-based clinical decision system for patients with osteosarcoma

“…Gun Woo Lee et al created a CNN model to diagnose spinal cord cervical spondylosis (CSM) by receiving input from multiple channels of two-dimensional data and performing iterative transformations using convolution and pooling operations to identify features of lateral cervical spine radiographs of patients with or without spinal cord cervical spondylosis (CSM) with high diagnostic accuracy (34). This also provides new ideas and references for the treatment and diagnosis of clinically relevant diseases (35)(36)(37).…”