ArticleNeuroImage2021
Deep learning model of fMRI connectivity predicts PTSD symptom trajectories in recent trauma survivors.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 32 citations in OpenAlex.
- Predicting longitudinal post-traumatic stress disorder symptoms using psychological measures and heart rate variability: a machine-learning approach.European journal of psychotraumatology · 2026Article
- Connectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors.JAMA network open · 2025Article
- [A study on post-traumatic stress disorder classification based on multi-atlas multi-kernel graph convolutional network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis.NPJ digital medicine · 2024Review
- Article
- Graph theory-based analysis reveals neural anatomical network alterations in chronic post-traumatic stress disorder.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.NeuroImage · 2023Article
- DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation.Journal of digital imaging · 2023Article
- Towards assessing subcortical "deep brain" biomarkers of PTSD with functional near-infrared spectroscopy.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Evaluating the Evidence for Brain-Based Biotypes of Psychiatric Vulnerability in the Acute Aftermath of Trauma.The American journal of psychiatry · 2023Article
- Artificial intelligence in adolescents mental health disorder diagnosis, prognosis, and treatment.Frontiers in public health · 2023Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Identifying Neurobiological Markers of Posttraumatic Stress Disorder Using Resting-State Functional Magnetic Resonance Imaging Data: The Promise of Data-Driven Computational Approaches.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2022Article
- Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.Frontiers in neuroimaging · 2022Review
- Brain correlates of depression, post-traumatic distress, and inflammatory biomarkers in COVID-19 survivors: A multimodal magnetic resonance imaging study.Brain, behavior, & immunity - health · 2021Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.