Highlights
We propose a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD one, six and 14-months after the exposure.
The method utilizes connectivity maps extracted from pairs of brain regions which are subsequently updated by applying the algorithmic technique of pairwise attention.
The proposed deep learning method predicts PTSD status, PTSD symptom clusters and survival analysis within the prospective design. We demonstrate a significant improvement in performance on all the datasets and experiments in comparison to other relevant analytical techniques.
Pairwise association analysis reveals several significant functional connectivity patterns, in line with previous PTSD neuroimaging literature.