A protocol for MIndfulness-based Neurofeedback to augment DBT psychotherapy for adults with Borderline Personality Disorder (MIND-BPD)
Bibliographic record
Abstract
Borderline personality disorder (BPD) is a severe psychiatric condition associated with high rates of suicide and poor interpersonal functioning. There are no FDA-approved medications, and evidence-based psychotherapies such as dialectical behavior therapy (DBT) are difficult to access and vulnerable to patient dropout. Novel treatment directions are urgently needed. Mindfulness is the core skill in DBT. Our team has developed a mindfulness-based real-time neurofeedback (mbNF) paradigm where individuals learn to reduce default mode network (DMN) versus control network activation in order to increase present-moment awareness. Here, we describe the study protocol for the MIndfulness-based Neurofeedback to Augment DBT Psychotherapy for Borderline Personality Disorder (MIND-BPD) trial. Participants with BPD (N = 52) enrolled in the study will be randomly assigned (1:1 ratio) to receive one session of either real or sham mbNF. Following neurofeedback, all participants will be enrolled in a 6-month DBT psychotherapy group. Supported by a R61/R33 grant from the NIH, the primary outcome of the R61 phase of this trial is change in DMN connectivity between pre- and post-NF resting-state scans, as defined by increased within-network connectivity between the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), and increased connectivity between the mPFC and dorsolateral prefrontal coftex (dlPFC). Change in self-reported mindfulness between pre- and post-NF is a key secondary outcome. This study is registered in the US Clinical Trials Registry (NCT06446765).
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.088 | 0.019 |
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".