Interconnections and global transitions among functional states encode activity-related dynamics as brain topology changes after yoga training

Abstract With the emphasis on sustainable health, understanding the neural dynamics associated with sustainable practices such as widely practiced yoga has gained significant importance. In this work, we explored the underlying neural mechanisms of yoga training by means of electroencephalogram reco...

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Bibliographic Details
Main Authors: Sandeep Bodda, Shyam Diwakar
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-025-00134-y
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Summary:Abstract With the emphasis on sustainable health, understanding the neural dynamics associated with sustainable practices such as widely practiced yoga has gained significant importance. In this work, we explored the underlying neural mechanisms of yoga training by means of electroencephalogram recordings. The EEG data was recorded before and after the yoga training of 13 participants, for a total of 39 trials, with each trial recorded on consecutive days. The temporal analysis was performed by means of microstates and the changes in the oscillatory rhythms were also evaluated via spectral and statistical analysis. Spectral analysis revealed changes in the oscillatory rhythms of β,γ,α,θ over the electrode regions of O2, P8 and FC6. An analysis of the changes in the temporal microstates revealed > 65% global variance in the topographic clusters, with a significant effect on the occurrence and time coverage parameters of the microstates before and after yoga training. This study highlights that yoga training significantly influences microstate dynamics associated with brain regions, including the visual network, insular cortex, and frontal gyrus, thereby potentially enhancing functions related to attention and cognitive decisions. These findings may suggest a multinetwork neurophysiological basis for the role of yoga in improving mental focus and adaptive decision processes.
ISSN:2045-2322