Brain correlates of alexithymia in eating disorders: A voxel‐based morphometry study
Bibliographic record
Abstract
AIMS: Alexithymia is a personality trait that consists of difficulty in identifying and acknowledging one's own and others' feelings. Recent studies reported that alexithymia is present in both anorexia (AN) and bulimia nervosa (BN). Brain morphological studies on healthy subjects showed that alexithymia correlates with several brain regions involved in emotions processing. The aim of this study was to investigate the anatomical correlates of alexithymia in AN and BN. METHODS: We performed a voxel-based morphometry study on 21 patients with AN and 18 with BN. Seventeen healthy subjects were used as a control group. Alexithymia, depression and anxiety were assessed with self-administered questionnaires and correlated to gray matter (GM) density in each group. RESULTS: In BN, alexithymia was correlated with the GM of the parietal lobe, in particular of the right angular gyrus. The correlation was predominantly linked with Difficulty Describing Feelings. In AN, we did not find correlations between GM and alexithymia. CONCLUSIONS: In BN, our results support the hypothesis that this trait may represent a relevant pathogenic or maintenance factor that contributes to relational difficulties, present in this pathology. In AN, the lack of correlation between GM volume and alexithymia may be influenced by atrophy in several brain regions that in turn can be, as previously reported, a consequence of caloric restriction. Also, the nature of alexithymia may be different from that of BN and controls and this trait could be secondary to a psychopathologic process specific to AN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".