Estimating brain age using high-resolution pattern recognition: Younger brains in long-term meditation practitioners

Neuroimage. 2016 Jul 1;134:508-513. doi: 10.1016/j.neuroimage.2016.04.007. Epub 2016 Apr 11.


Normal aging is known to be accompanied by loss of brain substance. The present study was designed to examine whether the practice of meditation is associated with a reduced brain age. Specific focus was directed at age fifty and beyond, as mid-life is a time when aging processes are known to become more prominent. We applied a recently developed machine learning algorithm trained to identify anatomical correlates of age in the brain translating those into one single score: the BrainAGE index (in years). Using this validated approach based on high-dimensional pattern recognition, we re-analyzed a large sample of 50 long-term meditators and 50 control subjects estimating and comparing their brain ages. We observed that, at age fifty, brains of meditators were estimated to be 7.5years younger than those of controls. In addition, we examined if the brain age estimates change with increasing age. While brain age estimates varied only little in controls, significant changes were detected in meditators: for every additional year over fifty, meditators' brains were estimated to be an additional 1month and 22days younger than their chronological age. Altogether, these findings seem to suggest that meditation is beneficial for brain preservation, effectively protecting against age-related atrophy with a consistently slower rate of brain aging throughout life.

Keywords: Aging; Brain; Gray matter; MRI; Meditation; Mindfulness.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aging*
  • Brain / anatomy & histology*
  • Brain / physiology*
  • Female
  • Humans
  • Machine Learning
  • Magnetic Resonance Imaging
  • Male
  • Middle Aged
  • Mindfulness
  • Negotiating*
  • Pattern Recognition, Automated
  • Young Adult