Emotion Processing Deficit in Euthymic Bipolar Disorder: A Potential Endophenotype
Bibliographic record
Abstract
Background: Emotion processing deficits have been described in patients with bipolar disorder (BD) and are considered one of the core cognitive abnormalities in BD with endophenotype potential. However, the literature on specific impairments in emotion processing cognitive strategies (directive/cortical/higher versus intuitive/limbic/lower) in euthymic adult BD patients and healthy first-degree relatives/high-risk (HR) subjects in comparison with healthy controls (HCs) is sparse. Methods: = 30) matched for age (years), years of education, and sex using computer-administered face emotions-Matching And Labeling Task (eMALT). Results: The three groups were significantly different based on labeling accuracy scores for fear and anger (FA) (P < 0.001) and sad and disgust (SD) (P < 0.001). On post-hoc analysis, HR subjects exhibited a significant deficit in the labeling accuracy of FA facial emotions (P < 0.001) compared to HC. The BD group was found to have significant differences in all FA (P = 0.004) and SD (P = 0.003) emotion matching as well as FA (P = 0.001) and SD (P < 0.001) emotion labeling accuracy scores. Conclusions: BD in remission exhibits FERD in general, whereas specific labeling deficits of fear and anger emotions, indicating impaired directive higher order aspect of emotion processing, were demonstrated in HR subjects. This appears to be a potential endophenotype. These deficits could underlie the pathogenesis in BD, with possible frontolimbic circuitry impairment. They may have potential implications in functional recovery and prognosis of BD.
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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".