Identifying Disease-Specific Neural Reactivity to Psychosocial Stress in Borderline Personality Disorder
Bibliographic record
Abstract
BACKGROUND: Patients with borderline personality disorder (BPD) typically present emotion dysregulation (ED) when faced with adversity. However, it is argued that altered stress response may be more influenced by ED than BPD-specific traits. Here, we investigated this issue with functional magnetic resonance imaging using another ED condition as clinical control, i.e., bipolar disorder (BD), and controlling for ED traits. METHODS: We recruited 17 patients with BD, 24 patients with BPD, and 32 healthy control (HC) subjects. We adapted a functional magnetic resonance imaging-compatible psychosocial stressor task (Montreal Imaging Stress Task) in which participants are placed under time pressure when performing mental calculations and then receive immediate performance feedback (positive, negative, and neutral). ED traits were measured via self-report questionnaires targeting cognitive emotion dysregulation, affective lability, and trait anger and anxiety. RESULTS: Relative to patients with BD and HC subjects, patients with BPD exhibited overactive corticolimbic reactivity across all conditions, particularly in self-monitoring and emotion regulation regions such as the orbitofrontal cortex and anterior insula, even when controlling for ED. Conversely, patients with BD exhibited hypoactive corticolimbic reactivity to all feedback conditions compared with patients with BPD and HC subjects, even after controlling for ED. HC subjects exhibited significantly lower amygdala/hippocampus activity compared with both clinical groups, although this did not survive when controlling for ED. CONCLUSIONS: This study provides new insight into BPD-specific neural stress responding, suggesting hyperactive self- and emotion-regulatory neural psychosocial stress responding, independent of ED traits. The findings also highlight the importance of considering BPD as a diagnostic profile distinguishable from other ED disorder clinical groups.
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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) | 0.001 | 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".