Shame on the brain: Neural correlates of moral injury event recall in posttraumatic stress disorder
Bibliographic record
Abstract
BACKGROUND: Moral injury (MI) is consistently associated with adverse mental health outcomes, including the development of posttraumatic stress disorder (PTSD) and suicidality. METHODS: We investigated neural activation patterns associated with MI event recall using functional magnetic resonance imaging in participants with military and public safety-related PTSD, relative to civilian MI-exposed controls. RESULTS: MI recall in the PTSD as compared to control group was associated with increased neural activation among salience network nodes involved in viscerosensory processing and hyperarousal (right posterior insula, dorsal anterior cingulate cortex; dACC), regions involved in defensive responding (left postcentral gyrus), and areas responsible for top-down cognitive control of emotions (left dorsolateral prefrontal cortex; dlPFC). Within the PTSD group, measures of state and trait shame correlated negatively with activity among default mode network regions associated with self-related processing and moral cognition (dorsomedial prefrontal cortex; dmPFC) and salience network regions associated with viscerosensory processing (left posterior insula), respectively. CONCLUSIONS: These findings suggest that MI event processing is altered in military and public safety-related PTSD, relative to MI-exposed controls. Here, it appears probable that as individuals with PTSD recall their MI event, they experience a surge of blame-related processing of bodily sensations within salience network regions, including the right posterior insula and the dACC, which in turn, prompt regulatory strategies at the level of the left dlPFC aimed at increasing cognitive control and inhibiting emotional affect. These results are consistent with previous findings showing enhanced sensory processing and altered top-down control in PTSD samples during autobiographical memory recall.
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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 | 0.000 | 0.001 |
| 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".