Deep learning based diagnosis of PTSD using 3D-CNN and resting-state fMRI data

Background: The incidence rate of Posttraumatic stress disorder (PTSD) is currently increasing due to wars, terrorism, and pandemic disease situations. Therefore, accurate detection of PTSD is crucial for the treatment of the patients, for this purpose, the present study aims to classify individuals with PTSD versus healthy control. Methods: The resting-state functional MRI (rs-fMRI) scans of 19 PTSD and 24 healthy control male subjects have been used to identify the activation pattern in most affected brain regions using group-level independent component analysis (ICA) and t-test. To classify PTSD-affected subjects from healthy control six machine learning techniques including random forest, Naive Bayes, support vector machine, decision tree, K-nearest neighbor, linear discriminant analysis, and deep learning three-dimensional 3D -CNN have been performed on the data and compared. Results: The rs-fMRI scans of the most commonly investigated 11 regions of trauma-exposed and healthy brains are analyzed to observe their level of activation. Amygdala and insula regions are determined as the most activated regions from the regions-of-interest in the brain of PTSD subjects. In addition, machine learning techniques have been applied to the components extracted from ICA but the models provided low classification accuracy. The ICA components are also fed into the 3D -CNN model, which is trained with a 5-fold crossvalidation method. The 3D -CNN model demonstrated high accuracies, such as 98.12%, 98.25 %, and 98.00 % on average with training, validation, and testing datasets, respectively. Conclusion: The findings indicate that 3D -CNN is a surpassing method than the other six considered techniques and it helps to recognize PTSD patients accurately.