Dorsal Anterior Cingulate Thickness Is Related to Alexithymia in Childhood Trauma-Related PTSD
Bibliographic record
Abstract
Alexithymia, or "no words for feelings", is highly prevalent in samples with childhood maltreatment and posttraumatic stress disorder (PTSD). The dorsal anterior cingulate cortex (dACC) has been identified as a key region involved in alexithymia, early life trauma, and PTSD. Functional alterations in the dACC also have been associated with alexithymia in PTSD. This study examined whether dACC morphology is a neural correlate of alexithymia in child maltreatment-related PTSD. Sixteen adults with PTSD and a history of childhood sexual abuse, physical abuse, or exposure to domestic violence, and 24 healthy controls (HC) completed the Toronto Alexithymia Scale 20 (TAS-20) and underwent magnetic resonance imaging. Cortical thickness of the dACC was measured using FreeSurfer, and values were correlated with TAS-20 scores, controlling for sex and age, in both groups. Average TAS-20 score was significantly higher in the PTSD than the HC group. TAS-20 scores were significantly positively associated with dACC thickness only in the PTSD group. This association was strongest in the left hemisphere and for TAS-20 subscales that assess difficulty identifying and describing feelings. We found that increasing dACC gray matter thickness is a neural correlate of greater alexithymia in the context of PTSD with childhood maltreatment. While findings are correlational, they motivate further inquiry into the relationships between childhood adversity, emotional awareness and expression, and dACC morphologic development in trauma-related psychopathology.
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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".