Exploration of Brain Network Measures Across Three Meditation Traditions
Pankaj Pandey, Pragati Gupta, Krishna Prasad Miyapuram
NeuroRegulation September 29, 2022 DOI: 10.15540/nr.9.3.113 (opens in new tab) via OpenAlex
Summary
AI-generated from the abstractComparing three meditation traditions—Himalayan Yoga, Isha Shoonya, and Vipassana—using EEG to build functional brain networks and graph measures reveals distinct neural patterns. Classifying traditions against a control group with support vector machines achieved up to 90% accuracy (alpha band for Isha Shoonya). Key findings include higher delta connectivity in Vipassana meditators, stronger synchronization of left anterior frontal theta networks across traditions, greater gamma2 processing in Himalayan and Vipassana meditators, increased left frontal activity in theta and gamma bands for all meditators, and extensive modularity in gamma processing. The work suggests implications for neurotechnology to guide novice meditators.
Study at a glance
| Characteristics | Observational study Peer reviewed |
|---|---|
| Population | Meditators from Himalayan Yoga, Isha Shoonya, and Vipassana traditions, and a control group |
| Topics | Default mode network Meditation |
| Keywords | Electroencephalography Modularity biology Cognitive psychology |
| Citations | 10 |
| Key finding | Distinct EEG functional connectivity patterns differentiate three meditation traditions, with maximum classification accuracy of 90% in the alpha band for Isha Shoonya. |
Abstract
Research into the similarities and differences between various forms of meditation practice is still in its early stages. Here, utilizing functional connectivity and graph measures, we present our work examining three meditation traditions: Himalayan Yoga (HT), Isha Shoonya (SNY), and Vipassana (VIP). EEG activity of the meditative block is used to build functional brain connections to exploit the resulting networks between various meditation traditions and a control group. Support vector machine is employed for binary classification, and models are built with features generated via graph theory measures. We obtain maximum accuracy of 84.76% with gamma1, 90% with alpha, and 84.76% with theta in HT, SNY, and VIP, respectively. Our key findings involve (a) higher delta connectivity in Vipassana meditators, (b) synchronization of theta networks in the left hemisphere inspected to be stronger in the anterior frontal area across meditators, (c) greater involvement of gamma2 processing observed among Himalayan and Vipassana meditators, (d) increased left frontal activity contribution for all meditators in theta and gamma bands, and (e) modularity engaged extensively in gamma processing across all meditation traditions. Furthermore, we discuss the implication of this research for neurotechnology products to enable guided meditation among naive practitioners.