Mindfulness Training Changes Brain Dynamics During Depressive Rumination: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Depression is a leading cause of disability worldwide and its prevalence is on the rise. One of the most debilitating aspects of depression is the dominance and persistence of depressive rumination, a state of mind that is linked to onset and recurrence of depression. Mindfulness meditation trains adaptive attention regulation and present-moment embodied awareness, skills that may be particularly useful during depressive mind states characterized by negative ruminative thoughts. METHODS: In a randomized controlled functional magnetic resonance imaging study (N = 80), we looked at the neurocognitive mechanisms behind mindfulness-based cognitive therapy (n = 50) for recurrent depression compared with treatment as usual (n = 30) across experimentally induced states of rest, mindfulness practice and rumination, and the relationship with dispositional psychological processes. RESULTS: Mindfulness-based cognitive therapy compared with treatment as usual led to decreased salience network connectivity to the lingual gyrus during a ruminative state, and this change in salience network connectivity mediated improvements in the ability to sustain and control attention to body sensations. CONCLUSIONS: These findings showed that a clinically effective mindfulness intervention modulates neurocognitive functioning during depressive rumination and the ability to sustain attention to the body.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".