Brain stimulation enhances dispositional mindfulness in PTSD: an exploratory sham-controlled rTMS trial
Bibliographic record
Abstract
Objective: Post-traumatic stress disorder (PTSD) is characterized by hypervigilance, intrusive thoughts, negative mood, and avoidant behaviors. Therapies involving mindfulness have been shown to reduce PTSD symptoms and modulate brain function. Pharmacological and brain stimulation interventions are also effective for treating PTSD. Non-invasive repeated transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) has been shown to regulate mood and improve PTSD symptoms. Methods: This is a retrospective chart analysis of data collected pre-treatment, post-treatment, and at three-month follow-up in a single-site, double-blind, sham-controlled trial of right DLPFC rTMS. 31 participants diagnosed with PTSD were recruited for this pilot study. Over two weeks, 19 participants received ten sessions of either 1 Hz or 10 Hz stimulation, and nine received sham treatment. Results: Participants in the rTMS group had a significant reduction in total Five Facet Mindfulness Questionnaire (FFMQ) scores from baseline to post-treatment, this difference was no longer observed when a false discovery rate (FDR) correction was applied. However, a significant improvement was observed in the rTMS group from baseline to the three-month follow-up in total FFMQ score and nonreactivity. This change in mindfulness scores suggests a potential delay in onset of benefits. Conclusions: Based on our preliminary data, rTMS may improve levels of dispositional mindfulness and its specific subcomponents. Future studies could investigate brain stimulation to assess its utility for improving mindfulness and related health outcomes to reduce suffering related to PTSD. Moreover, application of this neurostimulation modality for improving mental illness and well-being more generally merits further exploration. Clinical trial registration: https://clinicaltrials.gov/study, identifier NCT01806168.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".