Long-term meditation practice may produce neuroplastic changes that make cognitive processing more efficient. Using fMRI, researchers compared brain connectivity in long-term (average 13,596 hours) and short-term (average 1,095 hours) Rajayoga meditators from the Brahma Kumaris while they performed an engaging task with audio-visual distractions. A machine learning classifier (Gradient Boosted Tree) distinguished the two groups with 77% accuracy based on functional connectivity metrics. Connectivity was higher in long-term meditators in visual areas, cerebellum, left rostral prefrontal cortex, and middle frontal gyrus, suggesting that extensive meditation practice can lead to more effortless cognitive processing.
Long-term Rajayoga meditators from the Brahma Kumaris tradition show distinct brain functional connectivity patterns compared to short-term practitioners, even while performing a non-meditative task. Using task-based fMRI data, graph-theoretical measures of functional connectivity (adjacency matrices, global efficiency, local efficiency) were calculated from 132 brain regions. Machine learning classifiers—especially decision tree, random forest, and gradient boosted tree—achieved over 84% test accuracy in distinguishing long-term (mean 13,596 hours of practice) from short-term (mean 1,095 hours) meditators. These findings suggest that extensive meditation practice produces lasting changes in brain network organization that persist outside of meditation.