Neural Correlates of Levels of Emotional Awareness During Trauma Script-Imagery in Posttraumatic Stress Disorder
Bibliographic record
Abstract
Objective: To examine individual differences in levels of emotional awareness as a predictor of the blood oxygenation level dependent (BOLD) response to trauma script-driven imagery in trauma-exposed individuals with (n = 25) and without (n = 16) posttraumatic stress disorder (PTSD). Methods: Participants completed the Levels of Emotional Awareness Scale (LEAS) and a functional magnetic resonance imaging trauma script-driven imagery paradigm. Results: Patients with PTSD exhibited lower LEAS scores in comparison with the control group. LEAS scores correlated positively with BOLD activity during trauma script-imagery in the ventral anterior cingulate cortex (vACC) in healthy controls, whereas LEAS scores correlated negatively with activation of vACC in individuals with PTSD. Conclusion: Patients with PTSD exhibit lower than average levels of emotional awareness. Levels of emotional awareness are differentially associated with vACC response during trauma script-driven imagery in healthy controls versus individuals with PTSD. PTSD = posttraumatic stress disorder; LEAS = Levels of Emotional Awareness Scale; vACC = ventral anterior cingulate cortex; dACC = dorsal anterior cingulate cortex; mPFC = medial prefrontal cortex; BA = Brodmann Area; DSM-IV = Diagnostic and Statistical Manual—4th Edition; BOLD = blood oxygenation level dependent; SVC = small volume corrected; fMRI = functional magnetic resonance imaging.
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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".