Emotion regulation in bipolar disorder type I: an fMRI study
Bibliographic record
Abstract
BACKGROUND: Bipolar disorder type I (BD-I) is associated with emotion dysregulation. However, experimentally controlled studies of emotion regulation (ER), particularly those examining the brain correlates of the putative deficits, are scarce and their results inconsistent. METHOD: Nineteen euthymic BD-I patients and 17 healthy controls (HC) underwent functional magnetic resonance imaging while performing a visual ER 2 × 2 factorial task, with instruction (Look or Decrease) and valence (Negative or Neutral) as within-subject factors. Emotional ratings were collected after each picture presentation to assess regulation success. RESULTS: BD-I patients were successful at downregulating their emotions, although to a lesser degree than HC. Both groups engaged brain regions previously implicated in ER; however, unlike HC, patients engaged some of those regions, particularly the ventrolateral prefrontal cortex (VLPFC) in the Negative Look and Neutral Decrease conditions. Moreover, patients failed to show the reduced amygdala activation in the Negative Decrease condition observed in HC. CONCLUSION: Our findings suggest that BD-I patients are able to downregulate their emotions when instructed to do so. However, they also appear to engage their ER network, particularly the VLPFC, even when not required to do so. These findings may help explain their often-reported difficulty in regulating emotions in everyday life despite their attempts to do so.
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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".