Grey matter alterations in patients with depersonalization disorder: a voxel-based morphometry study
Bibliographic record
Abstract
BACKGROUND: To our knowledge, no whole brain investigation of morphological aberrations in dissociative disorder is available to date. Previous region-of-interest studies focused exclusively on amygdalar, hippocampal and parahippocampal grey matter volumes and did not include patients with depersonalization disorder (DPD). We therefore carried out an explorative whole brain study on structural brain aberrations in patients with DPD. METHODS: We acquired whole brain, structural MRI data for patients with DPD and healthy controls. Voxel-based morphometry was carried out to test for group differences, and correlations with symptom severity scores were computed for grey matter volume. RESULTS: Our study included 25 patients with DPD and 23 controls. Patients exhibited volume reductions in the right caudate, right thalamus and right cuneus as well as volume increases in the left dorsomedial prefrontal cortex and right somatosensory region that are not a direct function of anxiety or depression symptoms. LIMITATIONS: To ensure ecological validity, we included patients with comorbid disorders and patients taking psychotropic medication. CONCLUSION: The results of this first whole brain investigation of grey matter volume in patients with a dissociative disorder identified structural alterations in regions subserving the emergence of conscious perception. It remains unknown if these alterations are best understood as risk factors for or results of the disorder.
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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 | 0.001 | 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) | 0.002 | 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".