PW01-150 - Ascertainment Of The Cerebral Bases Of Alexithymia Traits In Depersonalization Disorder
Bibliographic record
Abstract
Objectives The current study elucidates the relations between alexithymia and brain activation towards happy and sad emotional stimulation in Depersonalization Disorder (DPD). We hypothesized that various facets of the alexithymia construct are differentially related to single neural structures characterizing abnormal emotion processing in DPD. Methods Investigated were N = 9 patients with DPD and N = 12 normal controls. To establish the diagnosis of DPD, ICD-10 and scale cut-off values (CDS > 70) were included. Alexithymia was measured using the Toronto Alexithymia Scale (TAS-20). Implicit fMRI tasks inducing three steps of happy and sad facial expressions were run, and clinical trait scores were correlated with brain activation in each emotion category. Results Significant positive correlations for alexithymia levels in DPD patients were in the right hypothalamus (happy, r = 0.60, p < 0.00001) and right retrosplenial cortex (sad, r = 0.78, p < 0.00001). The prominent regions for alexithymia levels in normal controls were instead in the left cerebellum (happy, r = 0.71, p < 0.00001) and right ventrolateral cortex (sad, r = 0.89, p < 0.00001). Significant differences in the regression slopes for the two groups were observed in the left putamen (happy, average Δr = 0.86, p < 0.0255) and in the left dorsal ACC (sad, average Δr = 0.87, p < 0.0087). Conclusions The present results suggest that the alexithymia trait is related to specific brain regions dependent on emotion. DPD patients recruit different brain regions compared to normal controls in alexithymia.
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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) | 0.007 | 0.001 |
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".