Effects of trauma-related cues on pain processing in posttraumatic stress disorder: an fMRI investigation
Bibliographic record
Abstract
BACKGROUND: Imaging studies of pain processing in primary psychiatric disorders are just emerging. This study explored the neural correlates of stress-induced analgesia in individuals with posttraumatic stress disorder (PTSD). It combined functional magnetic resonance imaging (fMRI) and the traumatic script-driven imagery symptom provocation paradigm to examine the effects of trauma-related cues on pain perception in individuals with PTSD. METHODS: The study included 17 patients with PTSD and 26 healthy, trauma-exposed controls. Participants received warm (nonpainful) or hot (painful) thermal stimuli after listening to a neutral or a traumatic script while they were undergoing an fMRI scan at a 4.0 T field strength. RESULTS: Between-group analyses revealed that after exposure to the traumatic scripts, the blood oxygen level-dependent (BOLD) signal during pain perception was greater in the PTSD group than the control group in the head of the caudate. In the PTSD group, strong positive correlations resulted between BOLD signal and symptom severity in a number of brain regions previously implicated in stress-induced analgesia, such as the thalamus and the head of the caudate nucleus. Trait dissociation as measured by the Dissociative Experiences Scale correlated negatively with the right amygdala and the left putamen. LIMITATIONS: This study included heterogeneous traumatic experiences, a different proportion of military trauma in the PTSD versus the control group and medicated patients with PTSD. CONCLUSION: These data indicate that in patients with PTSD trauma recall will lead in a state-dependent manner to greater activation in brain regions implicated in stress-induced analgesia. Correlational analyses lend support to cortical hyperinhibition of the amygdala as a function of dissociation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".