The Neurobiology of Emotion Regulation in Personality Disorders
Bibliographic record
Abstract
Background: According to recent estimates, personality disorders (PDs) have a global pooled prevalence of 7.8%, making them an important social and economic burden.Reaching a better understanding of their development and treatment is thus of utmost importance.Research also suggests that individuals with a PD show neurobiological abnormalities that qualitatively deviate from normality in a variety of ways, including emotion regulation.Methods: In March 2023, a literature search was conducted in five bibliographic databases: Embase.com,Medline Ovid ALL, APA Psycinfo Ovid, Cochrane Library (all databases) and Web of Science Core Collection.Additional studies were identified by reviewing the bibliographic references of the retained articles.To be included, papers should study/report on neural correlates of emotion regulation in PDs in adult populations.Results: After reading the abstracts and excluding publications prior to 2018, we identified 25 papers.After excluding theoretical papers as well as meta-analyses, the first author selected 13 articles of interest.They all either focused exclusively on borderline personality disorder (BPD) or merely used other PDs as psychopathological control groups.Discussion: Collectively, the reviewed articles form a coherent theory of dysfunctional emotion regulation in patients with BPD in the form of the fronto-limbic imbalance model, which is characterized by hypoactivity of the prefrontal region and hyperactivity of the limbic region.Thus, it appears that, compared to individuals without BPD, those with the diagnosis have a marked tendency to interpret their environment more negatively and to react with greater emotional intensity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".