Acute Stress Disorder Detection using Machine Learning based on resting-state fMRI

Early diagnosis of Acute Stress Disorder (ASD) is important, given its potential progression to post-traumatic system disorder (PTSD). The current diagnostic tool has some degree of subjectiveness in assessing emotional responses to trauma and the severity of stress reactions. To this end, we proposed a new method to detect ASD using machine learning with resting-state functional magnetic resonance imaging (rs-fMRI) data. We used 48 subjects of rs-fMRI data and PTSD Check List - Civilian Version (PCL-C) questionnaire from Advancing Understanding of RecOvery afteR traumA (AURORA) dataset. We extracted five frequency-domain features from each blood-oxygen-level dependent (BOLD) signal from 48 cortical and 21 subcortical regions. We also extracted four graph features from sparse inverse covariance matrices of the BOLD signals. Eighteen features appeared to be significantly different (p<0.05). Using these features, multi-layer perceptron showed accuracy 91.7%, sensitivity 96.8%, and specificity 82.4% using the leave-one-subject-out cross validation scheme. We found that the Right Accumbens and Lingual Gyrus has high effect size and substantial impact within the machine learning model.