Deep learning model of fMRI connectivity predicts PTSD symptom trajectories in recent trauma survivors

Shelly Sheynin, Lior Wolf*, Ziv Ben-Zion, Jony Sheynin, Shira Reznik, Jackob Nimrod Keynan, Roee Admon, Arieh Shalev, Talma Hendler, Israel Liberzon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Early intervention following exposure to a traumatic life event could change the clinical path from the development of post traumatic stress disorder (PTSD) to recovery, hence the interest in early detection and underlying biological mechanisms involved in the development of post traumatic sequelae. We introduce 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 symptoms at one-, six- and fourteen-months after the exposure. FMRI data, as well as PTSD status and symptoms, were collected from adults at risk for PTSD development, after admission to emergency room following a traumatic event. Our computational method utilized a per-region encoder to extract brain regions embedding, which were subsequently updated by applying the algorithmic technique of pairwise attention. The affinities obtained between each pair of regions were combined to create a pairwise co-activation map used to perform multi-label classification. The results demonstrate that the novel method's performance in predicting PTSD symptoms, in a prospective manner, outperforms previous analytical techniques reported in the fMRI literature, all trained on the same dataset. We further show a high predictive ability for predicting PTSD symptom clusters and PTSD persistence. To the best of our knowledge, this is the first deep learning method applied on fMRI data with respect to prospective clinical outcomes, to predict PTSD status, severity and symptom clusters. Future work could further delineate the mechanisms that underlie such a prediction, and potentially improve single patient characterization.

Original languageEnglish
Article number118242
JournalNeuroImage
Volume238
DOIs
StatePublished - Sep 2021

Funding

FundersFunder number
ISF Israel Precision Medicine2923/20
U.S. Department of DefenseW81XWH-16-C-0198
National Institute of Mental HealthR01MH103287
European Research Council
Israel Science Foundation
Horizon 2020ERC CoG 725974

    Keywords

    • Attention mechanism
    • Deep learning
    • End-to-end neural network
    • PTSD symptom clusters
    • fMRI

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