Improving treatment outcomes for borderline personality disorder: what can we learn from biomarker studies of psychotherapy?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Borderline personality disorder (BPD) is a severe and common psychiatric disorder and though evidence-based psychotherapies are effective, rates of treatment nonresponse are as high as 50%. Treatment studies may benefit from interdisciplinary approaches from neuroscience and genetics research that could generate novel insights into treatment mechanisms and tailoring interventions to the individual. RECENT FINDINGS: We provide a timely update to the small but growing body of literature investigating neurobiological and epigenetic changes and using biomarkers to predict outcomes from evidence-based psychotherapies for BPD. Using a rapid review methodology, we identified eight new studies, updating our earlier 2018 systematic review. Across all studies, neuroimaging ( n = 18) and genetics studies ( n = 4) provide data from 735 participants diagnosed with BPD (mean sample size across studies = 33.4, range 2-115). SUMMARY: We report further evidence for psychotherapy-related alterations of neural activation and connectivity in regions and networks relating to executive control, emotion regulation, and self/interpersonal functioning in BPD. Emerging evidence also shows epigenetic changes following treatment. Future large-scale multisite studies may help to delineate multilevel treatment targets to inform intervention design, selection, and monitoring for the individual patient via integration of knowledge generated through clinical, neuroscience, and genetics research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".