Amygdala reactivity to masked negative faces is associated with automatic judgmental bias in major depression: a 3 T fMRI study
Bibliographic record
Abstract
OBJECTIVE: In a previous study, we demonstrated that amygdala reactivity to masked negative facial emotions predicts negative judgmental bias in healthy subjects. In the present study, we extended the paradigm to a sample of 35 inpatients suffering from depression to investigate the effect of amygdala reactivity on automatic negative judgmental bias and clinical characteristics in depression. METHODS: Amygdala activity was recorded in response to masked displays of angry, sad and happy facial expressions by means of functional magnetic resonance imaging at 3 T. In a subsequent experiment, the patients performed an affective priming task that characterizes automatic emotion processing by investigating the biasing effect of subliminally presented emotional faces on evaluative ratings to subsequently presented neutral stimuli. RESULTS: Significant associations between (right) amygdala reactivity and automatic negative judgmental bias were replicated in our patient sample (r=-0.59, p<0.001). Further, negatively biased evaluative processing was associated with severity and longer course of illness (r=-0.57, p=0.001). CONCLUSION: Amygdala hyperactivity is a neural substrate of negatively biased automatic emotion processing that could be a determinant for a more severe disease course.
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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".