Sad mood induction has an opposite effect on amygdala response to emotional stimuli in euthymic patients with bipolar disorder and healthy controls
Bibliographic record
Abstract
BACKGROUND: Aberrant amygdala reactivity to affective stimuli represents a candidate factor predisposing patients with bipolar disorder (BD) to relapse, but it is unclear to what extent amygdala reactivity is state-dependent. We evaluated the modulatory influence of mood on amygdala reactivity and functional connectivity in patients with remitted BD and healthy controls. METHODS: Amygdala response to sad versus neutral faces was investigated using fMRI during periods of normal and sad mood induced by autobiographical scripts. We assessed the functional connectivity of the amygdala to characterize the influence of mood state on the network responsible for the amygdala response. RESULTS: We included 20 patients with remitted BD and 20 controls in our study. The sad and normal mood exerted opposite effects on the amygdala response to emotional faces in patients compared with controls (F1,38 = 5.85, p = 0.020). Sad mood amplified the amygdala response to sad facial stimuli in controls but attenuated the amygdala response in patients. The groups differed in functional connectivity between the amygdala and the inferior prefrontal gyrus (p ≤ 0.05, family-wise error-corrected) of ventrolateral prefrontal cortex (vlPFC) corresponding to Brodmann area 47. The sad mood challenge increased connectivity during the period of processing sad faces in patients but decreased connectivity in controls. LIMITATIONS: Limitations to our study included long-term medication use in the patient group and the fact that we mapped only depressive (not manic) reactivity. CONCLUSION: Our results support the role of the amygdala-vlPFC as the system of dysfunctional contextual affective processing in patients with BD. Opposite amygdala reactivity unmasked by the mood challenge paradigm could represent a trait marker of altered mood regulation in patients with 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".