It is natural to feel afraid and experience a range of emotional reactions after a trauma, but most people recover and learn to cope with the trauma naturally. Unfortunately, some individuals continue to experience stressful symptoms and it is not known why these individuals chronically persist in having symptoms and develop post-traumatic stress disorder (PTSD). PTSD is a serious deliberating disease that not only affects the individual, but also surrounding friends and family. The disease also has a huge societal impact. From 2014 to 2017, the Danish Defence spent more than 1 billion DKK as compensation to combat veterans with PTSD.
The diagnosis of PTSD is mainly based on self-report in clinical interviews and first-line treatment is psychotherapy, but even though various psychotherapies effectively reduces the symptoms of PTSD, they are often not enough to fully “cure” the patient as around 2/3 of all patients still retain their PTSD diagnosis after treatment. One reason for the high non-response rate could be that PTSD is a heterogenous and complex disease, which is not fully captured by the self-reports, thus discovery of objective indicators of PTSD is in high-demand.
The aim of my project is characterization of potential features associated with PTSD, i.e. biomarkers for the disease. Specifically, I will explore resting-state electroencephalography (EEG) data from Danish combat veterans, as they have a particularly high risk for PTSD due to the high occurrences of traumas associated with their profession, and look for different patterns of neural activity between combat veterans with and without PTSD. EEG signal is highly complex and I will employ machine learning algorithms to search for potential biomarkers that can be used to correctly classify and predict the disease.
Unraveling objective biomarkers for PTSD would not only broaden our understanding of the disease and unlock new treatment candidate targets, but could also improve diagnosis and enable early intervention.