Brain structural correlates of alexithymia in patients with major depressive disorder
Bibliographic record
Abstract
Background: Alexithymia is a risk factor for major depressive disorder (MDD) and has been associated with diminished treatment response. Neuroimaging studies have revealed structural aberrations of the anterior cingulate cortex and the fusiform gyrus in healthy controls with high levels of alexithymia. The present study tried to corroborate and extend these results to patients with MDD compared with healthy controls. Methods: We investigated the relationship between alexithymia, depression and grey matter volume in 63 patients with MDD (mean age ± standard deviation = 42.43 yr ± 11.91; 33 female) and 46 healthy controls (45.35 yr ± 8.37; 22 female). We assessed alexithymia using the Toronto Alexithymia Scale. We conducted an alexithymia × group analysis of covariance; we used a region-of-interest approach, including the fusiform gyrus and anterior cingulate cortex, and conducted whole brain analysis using voxelbased morphometry. Results: Our analysis revealed a significant alexithymia × group interaction in the fusiform gyrus (left, pFWE = 0.031; right, pFWE = 0.010). Higher alexithymia scores were associated with decreased grey matter volume in patients with MDD (pFWE = 0.009), but with increased grey matter volume of the fusiform gyrus in healthy controls (pFWE = 0.044). We found no significant main effects in the region-of-interest analysis. Limitations: Owing to the naturalistic nature of our study, patients with MDD and healthy controls differed significantly in their alexithymia scores. Conclusion: Our results showed the fusiform gyrus as a correlate of alexithymia. We also found differences related to alexithymia between patients with MDD and healthy controls in the fusiform gyrus. Our study encourages research related to the transition from risk to MDD in people with 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 | 0.001 | 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".