Neural correlates of alexithymia in response to emotional stimuli: a study of anorexia nervosa patients.
Bibliographic record
Abstract
Individuals with alexithymia are typically unable to identify, understand, or describe their own emotions. Patients with anorexia nervosa (AN) have been shown to have high levels of alexithymia, and the latter trait may play an important role over the course of AN. However, relatively little is known about the underlying neurobiological relationships between alexithymia and AN. The aim of this study was to investigate the relationship between alexithymia level and brain activation in patients with AN. Thirty female patients participated in this study. Alexithymia was measured using the 20-item Toronto Alexithymia Scale. Functional magnetic resonance imaging was used to identify the brain regions that display abnormal hemodynamic activity while patients with AN were engaged in an emotional decision-making task. There was significant activation in the amygdala during the task, but not in the posterior and anterior cingulate cortices (PCC, ACC). However, PCC and ACC activation did vary as a function of alexithymia level. These results suggest that alexithymia in AN patients is associated with a deficit in the cognitive evaluation of negative emotions concerning body image. Alexithymia might play a crucial role in the emotional processing impairments that are often observed in AN patients, and this trait might ultimately help to better account for the psychopathological mechanism that underlies 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".