Increased Activation of the Supragenual Anterior Cingulate Cortex during Visual Emotional Processing in Male Subjects with High Degrees of Alexithymia: An Event-Related fMRI Study
Bibliographic record
Abstract
BACKGROUND: One of the most prominent neurobiological models of alexithymia assumes an altered function of the anterior cingulate cortex (ACC) as the crucial neural correlate of alexithymia. So far functional imaging studies have yielded inconclusive results. Therefore, we tested this hypothesis in healthy alexithymics and nonalexithymics in an event-related fMRI study. METHODS: Thirty high- and 30 low-alexithymic right-handed male subjects (selected by the 20-item Toronto Alexithymia Scale, TAS-20) were investigated with event-related fMRI using a picture viewing paradigm. The stimuli consisted of happy, fearful and neutral facial expressions (Ekman-Friesen) as well as positive, negative and neutral pictures from the International Affective Picture System. RESULTS: Contrasting the high-alexithymic with the low-alexithymic group we observed increased activation of the supragenual ACC for different emotional valences as well as for different emotional stimuli. Moreover, there was a positive correlation of the ACC with the individual TAS-20 scores but no correlations with the individual Beck Depression Inventory scores. Additionally, there was no difference in activity of the amygdala. CONCLUSIONS: We demonstrated that the supragenual ACC is constantly activated more strongly in alexithymic subjects and that this activation is related to the symptoms of alexithymia and not to associated symptoms such as depression. Therefore, our findings support the hypothesis of an altered function of the ACC 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 | 0.000 |
| 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) | 0.002 | 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".