Meditation Enhances Small-World Network Efficiency: MEG-Based Evidence from Attention and Control Systems

Brain networks and meditation have recently gained attention, with studies suggesting that more efficiently organized meditation brain networks are linked to better cognitive performance. This efficiency is exemplified in small-world brain networks, which combine local segregation with global integration, facilitating optimal information processing. This study examines the small-world propensity (SWP), a marker of neural efficiency, among three functional brain networks: the default mode network (DMN), fronto-parietal network (FPN), and attention network (AN) in three groups: advanced meditators (AM), beginner meditators (BM), and control meditators (CM).

Using magnetoencephalography (MEG), we recorded 10-min meditation sessions from AM and BM groups practicing Surat-Shabda-Yoga meditation at different stages. The CM (baseline group), with no formal training in meditation, had introductory exposure to "four chakra meditation" and practiced the same. SWP was computed using coherence-based connectivity measures across frequencies ranging from 4 to 45 Hz during stable meditative states.

Significant differences were observed between meditators and non-meditators, with AM and BM groups compared with the CM group. Specifically, the AN in the AM group compared with CM exhibited higher SWP at the beta frequency range (19 Hz), while the FPN in the BM group compared with the CM showed increased SWP at the theta frequency range (8 Hz).

These findings highlight how meditation engages the brain's intrinsic network architecture in a frequency- and stage-specific manner, supporting efficient information processing and offering a scientific basis for its cognitive and regulatory benefits.