Neural response to emotional faces in monozygotic twins: association with familial risk of affective disorders
Bibliographic record
Abstract
Background: Aberrant neural and cognitive response to emotional faces has been observed in people at familial risk of an affective disorder. In this functional MRI study of monozygotic twins, we explored neural correlates of the attentional avoidance of emotional faces that we had previously observed in high-risk versus affected twins, and whether an abnormal neural response to emotional faces represents a risk endophenotype. Methods: We recruited unaffected monozygotic twins with a co-twin history of mood episodes (high-risk), monozygotic twins with previous mood episodes (affected) and monozygotic twins with no personal or first-degree history of mood episodes (low-risk) between December 2014 and January 2017 based on a nationwide register linkage. Participants viewed fearful and happy faces while performing a gender discrimination task during functional MRI (fMRI) and performed emotional faces dot-probe and facial expression recognition tasks outside the scanner. Results: A total of 129 monozygotic twins underwent whole-brain fMRI. Highrisk twins (n = 38) displayed greater medial and superior prefrontal response to emotional faces than affected twins (n = 62). This greater activity correlated with stronger attentional avoidance of emotional faces in high-risk twins. In contrast, high-risk and affected twins showed no aberrant neural activity to emotional faces compared with low-risk twins (n = 29). Limitations: A limitation of this study was its cross-sectional design. Conclusion: Greater recruitment of the medial and superior prefrontal cortex during implicit emotion processing in high-risk versus affected twins may represent a compensatory or resilience mechanism. In contrast, aberrant neural response to emotional faces does not seem to be a risk endophenotype for affective disorders.
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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 | 0.001 | 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".