Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms

Background: The present study aimed to apply multivariate pattern recognition methods to predict PTSD symptoms from whole-brain activation patterns during two contexts where the aversiveness of unpleasant pictures was manipulated by the presence or absence of safety cues. Methods Trauma-exposed participants were presented with neutral and mutilation pictures during fMRI collection. Before the presentation of pictures, a text informed the subjects that the pictures were fictitious (“safe context”) or real-life scenes (“real context”). We trained machine learning regression models (Gaussian process regression (GPR)) to predict PTSD symptoms in real and safe contexts. Results The GPR model could predict PTSD symptoms from brain responses to mutilation pictures in a real context but not a safe one. The brain regions with the highest contribution to the model were the occipito-parietal regions, including the superior parietal gyrus, inferior parietal gyrus, and supramarginal gyrus. Additional analysis showed that GPR regression models accurately predicted clusters of PTSD symptoms, nominally intrusion, avoidance, and alteration in cognition. As expected, we obtained very similar results as those obtained in a model predicting total PTSD symptoms. Conclusion These results are innovative by showing that machine learning applied to fMRI can predict not only PTSD total symptoms but also clusters of PTSD symptoms in a more aversive context. Furthermore, this approach was able to identify potential biomarkers for PTSD, especially in occipito-parietal regions.