P-246 - The neuroimaging of dissociative disorders
Bibliographic record
Abstract
Dissociative disorders are characterized by disturbances of integration of memory, perception, consciousness or identity. They were linked to psychological stress or trauma across various cultures. Dissociative amnesia and fugue, depersonalization disorder, dissociative identity disorder and dissociative disorder not otherwise specified (such as Ganser syndrome) belong to dissociative disorders in DSM-IV-TR. In contrast to DSM-IV-TR, ICD-10 also subsumes the conversion disorder under the category of dissociative (conversion) disorders. This work's objective is establishing greater recognition of the neural correlates of dissociative disorders. We review neuroimaging data pertaining to dissociative amnesia and fugue, depersonalization disorder, dissociative identity disorder (multiple personality disorder), Ganser syndrome and various forms of conversion disorder, which were obtained with functional and structural imaging techniques, including diffusion tensor imaging or magnetization transfer ratio measurements. In addition to own imaging data from patients with dissociative amnesia and fugue, a comprehensive review of the scientific literature on the neuroimaging of dissociative disorders was performed. Neuroimaging research data point to metabolic and sometimes even structural brain alterations in dissociative disorders, involving regions that are agreed upon to play roles in mnemonic processing, self referential processing (including body schema), perception, consciousness and/or emotional processing. The use of functional and newer structural brain imaging methods has improved and will continue to further our understanding of the neural correlates of dissociative disorders and has provided evidence that environmentally-driven (stress-related) alterations of cognition, identity, body schema, perception, affectivity and behavior are accompanied by metabolic and even structural brain changes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".