Differences in brain connectivity of meditators during assessing neurocognition via gamified experimental logic task: A machine learning approach.
Ashwini S Savanth, Vijaya Pa, Ajay K Nair, Bindu M Kutty
The neuroradiology journal June 1, 2023 DOI: 10.1177/19714009221129574 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 22 |
| Population | Proficient Rajayoga meditators from the Brahma Kumaris (long-term and short-term practitioners) |
| Topics | Meditation |
| Keywords | Classification FMRI Functional connectivity Machine learning |
| Key findings | A Gradient Boosted Tree classifier distinguished long-term from short-term meditators with 77% accuracy using functional connectivity metrics, with higher connectivity in visual areas, cerebellum, left rostral prefrontal cortex, and middle frontal gyrus in long-term meditators. |
Abstract
Meditation practices increase attention, memory, and self-awareness. The neuroscientific study of meditation has helped gain useful insights into the functional changes in the brain. In this study, we have assessed the performance of meditators with different years of practice while performing an engaging task rather than studying the meditation practice itself. This task helps assess many neural processes simultaneously and represents task performance in presence of multiple audio-visual distractors as in a real-life scenario. The long-term practice of meditation could bring neuroplastic changes in the way cognitive processing is carried out. It could be conscious and effortful in short-term practitioners and relatively unconscious and effortless in long-term practitioners. Our goal is to understand if it is possible to differentiate between long-term and short-term meditators solely based on their cognitive processing. A group of proficient Rajayoga meditators from the Brahma Kumaris were recruited based on their meditation experience-Long-Term Practitioners (n = 12, mean 13,596 h) and Short-Term Practitioners (n = 10, mean 1095 h). A task-based functional Magnetic Resonance Imaging was acquired while the subjects performed the task. Functional Connectivity Analysis was performed to derive the correlation measures to be used as features for classification. Five supervised Machine Learning algorithms Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosted Tree were used for classification. Among all the classifiers Gradient Boosted Tree performed the best with an accuracy of 77% when all the four Functional Connectivity Metrics were used. Connectivity in visual areas, cerebellum, left rostral prefrontal cortex, and middle frontal gyrus was found to be higher in long-term meditators. Such a classification demonstrates that long-term meditation practice brings about neuroplastic changes that influence cognitive processing.