Neuromarkers of Post-Traumatic Stress Disorder: A Systematic Review of Positron Emission Tomography Studies
Bibliographic record
Abstract
Posttraumatic stress disorder (PTSD) is a severe mental health condition that can arise following exposure to a traumatic event. The identification of biomarkers using imaging techniques, such as positron emission tomography (PET) scans, offers an alternative to increasingly complex diagnostic approaches. This systematic review aims to assess and summarise the evidence for molecular biomarkers in PTSD. Studies published before June 2025 that examined brain changes using PET imaging in adult PTSD patients, were identified from three electronic databases. Inclusion criteria included original human studies, a PTSD diagnosis, and inclusion of healthy controls (not exposed to trauma) and/or trauma-exposed controls who did not develop PTSD. Studies with incomplete reporting, non-English publications, and those lacking control groups were excluded. The risk of bias was assessed using the Newcastle-Ottawa Scale. After screening the 1102 retrieved articles, 20 met the inclusion criteria. These involved 483 patients and 522 controls, including 426 healthy controls and 96 trauma-exposed controls. PTSD was associated with increased glucose metabolism in the amygdala and dorsal anterior cingulate cortex, alongside hypometabolism of the hippocampus and precuneus. Alterations in neurotransmitter systems, stress and inflammation systems, as well as elevated amyloid-beta accumulation, were also observed in PTSD. This review highlights converging neurobiological alterations in PTSD, including changes in metabolism, neurotransmission, and inflammation. These alterations predominantly involve large-scale brain networks such as the salience network, default mode network, and frontoparietal network, reflecting disruptions in emotion regulation, self-referential processing, and executive control. PET imaging shows promise for improving diagnosis, monitoring treatment response, and informing personalised interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".