The Effects of Meditation on Grey Matter Atrophy and Neurodegeneration: A Systematic Review
Bibliographic record
Abstract
The present systematic review is based on the premise that a variety of neurodegenerative diseases are accompanied by grey matter atrophy in the brain and meditation may impact this. Given that age is a major risk factor for many of these progressive and neurodegenerative diseases and that the percentage of the population over the age of 65 is quickly increasing, there is an obvious need for prompt treatment and prevention advances in research. As there is currently no cure for Alzheimer's disease and other neurodegenerative diseases, many are seeking non-pharmacological treatment options in attempts to offset the disease-related cognitive and functional declines. On the basis of a growing body of research suggesting that meditation is effective in increasing grey matter volume in healthy participants, this paper systematically reviewed the literature regarding the effects of meditation on restoring grey matter volume in healthy individuals and those affected by neurodegeneration. This review searched PubMed, CINAHL, and APA PsycNET to identify original studies that included MRI imaging to measure grey matter volume in meditators and post-mindfulness-based intervention participants compared to controls. Thirteen studies were considered eligible for review and involved a wide variety of meditation techniques and included participants with and without cognitive impairment. All studies reported significant increases in grey matter volume in the meditators/intervention group, albeit in assorted regions of the brain. Limited research exists on the mechanisms through which meditation affects disease-related neurodegeneration, but preliminary evidence suggests that it may offset grey matter atrophy.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".