ArticleScientific reports2020
Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.
- Artificial intelligence techniques applied to anxiety disorders recognition: a systematic review.Frontiers in digital health · 2025Pooled it
- Artificial intelligence role in advancement of human brain connectome studies.Frontiers in neuroinformatics · 2024Pooled it
- Explainable AI for Depression Detection and Severity Classification From Activity Data: Development and Evaluation Study of an Interpretable Framework.JMIR mental health · 2025Article
- Prediction and Feature Selection of Mastectomy-Related Post Traumatic Stress Disorder (PTSD) Using Machine Learning Among Breast Cancer Patients in Bangladesh.Cancer informatics · 2025Article
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- Central stress pathways in the development of cardiovascular disease.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2024Review
- Systematic review of machine learning in PTSD studies for automated diagnosis evaluation.Npj mental health research · 2023Review
- Sleepy Mice Case Study: Implementation and Assessment.Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience · 2023Article
- Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work.JMIR formative research · 2022Article
- MEG neural signature of sexual trauma in women veterans with PTSD.Experimental brain research · 2022Article
- Classification of posttraumatic stress disorder and related outcomes in women veterans using magnetoencephalography.Experimental brain research · 2022Article
- Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder.Frontiers in neuroinformatics · 2022Article
- A multi-head self-attention deep learning approach for detection and recommendation of neuromagnetic high frequency oscillations in epilepsy.Frontiers in neuroinformatics · 2022Article
- Teasing apart trauma: neural oscillations differentiate individual cases of mild traumatic brain injury from post-traumatic stress disorder even when symptoms overlap.Translational psychiatry · 2021Article
- Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.Human brain mapping · 2021Article
- A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression With MEG Brain Networks.IEEE transactions on bio-medical engineering · 2021Article
- The impact of psychopathology, social adversity and stress-relevant DNA methylation on prospective risk for post-traumatic stress: A machine learning approach.Journal of affective disorders · 2021Article
- Magnetoencephalography in the Detection and Characterization of Brain Abnormalities Associated with Traumatic Brain Injury: A Comprehensive Review.Medical sciences (Basel, Switzerland) · 2021Review
- Classification of Trauma-Related Outcomes in US Veterans Using Magnetoencephalography (MEG).Journal of neurology & neuromedicine · 2021Article
- Mild traumatic brain injury is associated with dysregulated neural network functioning in children and adolescents.Brain communications · 2021Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given the subjective nature of conventional diagnostic methods for post-traumatic stress disorder (PTSD), an objectively measurable biomarker is highly desirable; especially to clinicians and researchers. Macroscopic neural circuits measured using magnetoencephalography (MEG) has previously been shown to be indicative of the PTSD phenotype and severity. In the present study, we employed a machine learning-based classification framework using MEG neural synchrony to distinguish combat-related PTSD from trauma-exposed controls. Support vector machine (SVM) was used as the core classification algorithm. A recursive random forest feature selection step was directly incorporated in the nested SVM cross validation process (CV-SVM-rRF-FS) for identifying the most important features for PTSD classification. For the five frequency bands tested, the CV-SVM-rRF-FS analysis selected the minimum numbers of edges per frequency that could serve as a PTSD signature and be used as the basis for SVM modelling. Many of the selected edges have been reported previously to be core in PTSD pathophysiology, with frequency-specific patterns also observed. Furthermore, the independent partial least squares discriminant analysis suggested low bias in the machine learning process. The final SVM models built with selected features showed excellent PTSD classification performance (area-under-curve value up to 0.9). Testament to its robustness when distinguishing individuals from a heavily traumatised control group, these developments for a classification model for PTSD also provide a comprehensive machine learning-based computational framework for classifying other mental health challenges using MEG connectome profiles.
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