Gray matter volume alterations related to trait dissociation in <scp>PTSD</scp> and traumatized controls
Bibliographic record
Abstract
OBJECTIVE: This study used voxel-based morphometry (VBM) to investigate brain structural alterations related to trait dissociation and its relationship with post-traumatic stress disorder (PTSD). METHOD: Thirty-two subjects either developing (N = 15) or non-developing (N = 17) PTSD underwent MRI scanning and were assessed with the Dissociative Experience Scale (DES), subscales for pathological (DES-T) and non-pathological trait (DES-A) dissociation, and other clinical measures. Gray matter volume (GMV) was analyzed using VBM as implemented in SPM. PTSD and non-PTSD subjects were compared to assess brain alterations related to PTSD pathology, whereas correlation analyses between dissociation measures and GMV were performed on the whole sample (N = 32), irrespective of PTSD diagnosis, to identify alterations related to trait dissociation. RESULTS: As compared to traumatized controls, PTSD subjects showed reduced GMV in the prefrontal cortex, hippocampus and lingual gyrus. Correlations with dissociation measures (DES, DES-T, and DES-A) consistently showed increased GMV in the medial and lateral prefrontal, orbitofrontal, parahippocampal, temporal polar, and inferior parietal cortices. CONCLUSION: PTSD and dissociation seem to be associated with opposite volumetric patterns in the prefrontal cortex. Trait dissociation appears to involve increased GMV in prefrontal, paralimbic, and parietal cortices, with negligible differences between pathological and non-pathological dissociation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".