Abstract
Meditation practice is a non-pharmacological intervention that provides both physical and mental benefits. It has generated much neuroscientific interest in its effects on brain activity. Spontaneous brain activity can be measured by electroencephalography (EEG). Spectral powers of EEG signals are routinely mapped on a topographic layout of channels to visualize spatial variations within a certain frequency range. In this paper, we propose a node-based network filtration to model the spatial distribution of an EEG topographic power map via its dynamic local connectivity with respect to a changing scale. We compare topological features of the network filtrations between long-term meditators and mediation-naïve practitioners to investigate if long-term meditation practice changes power patterns in the brain.
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Acknowledgment
This work was supported by the National Center for Complementary and Alternative Medicine (NCCAM) P01AT004952. We also acknowledge the support of NIH grants UL1TR000427 and EB022856.
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Wang, Y., Chung, M.K., Dentico, D., Lutz, A., Davidson, R.J. (2017). Topological Network Analysis of Electroencephalographic Power Maps. In: Wu, G., Laurienti, P., Bonilha, L., Munsell, B. (eds) Connectomics in NeuroImaging. CNI 2017. Lecture Notes in Computer Science(), vol 10511. Springer, Cham. https://doi.org/10.1007/978-3-319-67159-8_16
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DOI: https://doi.org/10.1007/978-3-319-67159-8_16
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