Association of neuroimaging measures with facial emotional processing in healthy adults: a task fMRI study
Bibliographic record
Abstract
Investigating the neural processing of emotion-related neural circuits underlying emotional facial processing may help in understanding mental disorders. We used two subscales of the Toronto Alexithymia Scale (TAS) to assess the emotional cognitive of 25 healthy participants. A higher score indicates greater difficulty in emotional perception. In addition, participants completed a n-back task during functional magnetic resonance imaging. Psychophysiological interaction analysis was used to explore the functional connectivity (FC) of neural circuits. Next, we used elastic-net regression analysis for feature selection and conducted correlation analysis between the neuroimaging measures and questionnaire scores. Following a 3-fold cross-validation, five neuroimaging measures emerged as significant features. Results of correlation analysis demonstrated that participants with higher TAS scores exhibited increased FC between the amygdala and occipital face area during facial stimulus processing, but decreased connectivity during emotional processing. These findings suggested that individuals with poor emotional recognition exhibited increased connectivity among face-related brain regions during facial processing. However, during emotional processing, decreasing neural synchronization among neural circuits involved in emotional processing affects facial expression processing. These findings suggest potential neural marker related to subjective emotional perception, which may contribute to the diagnosis and treatment of emotional dysregulation in individuals with psychiatric conditions.
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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 | 0.000 | 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) | 0.001 | 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".