ArticleScientific reports2025
Towards predicting posttraumatic stress symptom severity using portable EEG-derived biomarkers.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Stress and alcohol impact network states involved in emotional processing: relevance of comorbid AUD and psychiatric illnesses.Psychopharmacology · 2026Pooled it
- Quantitative Electroencephalography as a Complement to Symptom-Based Psychiatric Diagnosis: A Narrative Review.Journal of personalized medicine · 2026Review
- Bridging Neural Topology and Affective Computing: Graph Attention for EEG Emotion Recognition.Journal of medical systems · 2026Review
- Rat Models in Post-Traumatic Stress Disorder Research: Strengths, Limitations, and Implications for Translational Studies.Pathophysiology : the official journal of the International Society for Pathophysiology · 2024Review
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9 authors.
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Abstract
Posttraumatic Stress Disorder (PTSD) is a heterogeneous mental health disorder that can develop following a traumatic experience. Understanding its neurobiological basis is crucial to advance early diagnosis and treatment. Electroencephalography (EEG) can be used to explore the neurobiological basis of PTSD. However, only limited research has explored mobile EEG, which is important for scalability. This proof-of-concept study delves into mobile EEG-derived biomarkers for posttraumatic stress (PTS) symptom severity and their potential implications. Participants with partial PTSD, defined as meeting for at least three out of four symptom clusters, including hyperarousal symptoms, were enrolled in the study. Over four weeks, we measured PTS symptom severity using the PTSD checklist for DSM-5 (PCL-5) at multiple timepoints, and we recorded multiple EEG sessions from 21 individuals using a mobile EEG device. In total, we captured 38 EEG sessions, each comprising two recordings ("Recording A" and "Recording B") that lasted approximately 180 s, to evaluate reproducibility. Next, we extracted Shannon entropy, as a measure of the brain flexibility and complexity of the signal and spectral power for the fronto-temporal regions of interest, including electrodes at AF3, AF4, T7, and T8 for each EEG recording session. We calculated the partial correlation between the EEG variables and PCL- 5 measured closest to the EEG session, using age, sex, and the grouping variable 'batch' as covariates. We observed a significant negative correlation between Shannon entropy in fronto- temporal regions and PCL-5 scores. Specifically, this association was evident in the AF3 (r = -0.456, FDR-corrected p = 0.01), AF4 (r = -0.362, FDR-corrected p = 0.04), and T7 (r = -0.472, FDR-corrected p = 0.01) regions. Additionally, we found a significant negative association between the alpha power estimated from AF4 and PCL-5 (r = -0.429, FDR-corrected p = 0.04). Our findings suggest that EEG markers acquired using a mobile EEG device are associated with PTS symptom severity, offering valuable insights into the neurobiological mechanisms underlying PTSD and highlighting the potential benefits of this innovative technology in assessing and monitoring PTSD.
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