Anterior cingulate cortical thickness is a stable predictor of recovery from post-traumatic stress disorder
Bibliographic record
Abstract
BACKGROUND: Decreased cortical thickness in frontal and temporal regions has been observed in individuals suffering from post-traumatic stress disorder (PTSD), compared to healthy controls and trauma-exposed participants without PTSD. In addition, individual differences, both functional and structural, in the anterior cingulate cortex (ACC) have been shown to predict symptom severity reduction. Although there is some evidence suggesting that activity in this region changes as a function of recovery, it remains unknown whether there are any structural correlates of recovery from PTSD. METHOD: Thirty participants suffering from moderate to severe PTSD underwent a magnetic resonance imaging (MRI) scan following an initial clinical assessment. A second assessment took place 6-9 months later. In addition, a subgroup of 25 participants completed a second MRI scan at that time. PTSD symptom severity changes over time were regressed against vertex-based cortical thickness. RESULTS: We found that cortical thickness in the right subgenual ACC (sgACC) predicted symptom improvement. Moreover, cortical thickness within this region of the ACC, measured 6-9 months later (n = 25), was also correlated with the same measure of symptom improvement. By contrast, no relationship was found between change in cortical thickness in this area and current PTSD symptom levels or degree of recovery. CONCLUSIONS: Our results suggest that sgACC thickness may be a stable marker of recovery potential in PTSD.
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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 | 0.003 |
| 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".