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Meditation-type specific reduction in infra-slow activity of electroencephalogram

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Abstract

Purpose

Meditation is renowned for its positive effects on cognitive abilities and stress reduction. It has been reported that the amplitude of electroencephalographic (EEG) infra-slow activity (ISA, < 0.1 Hz) is reduced as the stress level decreases. Consequently, we aimed to determine if EEG ISA amplitude decreases as a result of meditation practice across various traditions.

Methods

To this end, we analyzed an open dataset comprising EEG data acquired during meditation sessions from experienced practitioners in the Vipassana tradition—which integrates elements of focused attention and open monitoring, akin to mindfulness meditation—and in the Himalayan Yoga and Isha Shoonya traditions, which emphasize focused attention and open monitoring, respectively.

Results

A general trend was observed where EEG ISA amplitude tended to decrease in experienced meditators from these traditions compared to novices, particularly significant in the 0.03–0.08 Hz band for Vipassana meditators. Therefore, our analysis focused on this ISA frequency band. Specifically, a notable decrease in EEG ISA amplitude was observed in Vipassana meditators, predominantly in the left-frontal region. This reduction in EEG ISA amplitude was also accompanied by a decrease in phase-amplitude coupling (PAC) between the ISA phase and alpha band (8–12 Hz) amplitude, which implied decreased neural excitability fluctuations.

Conclusion

Our findings suggest that not only does EEG ISA amplitude decrease in experienced meditators from traditions that incorporate both focused attention and open monitoring, but this decrease may also signify a diminished influence of neural excitability fluctuations attributed to ISA.

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References

  1. Aladjalova NA. Infra-slow rhythmic oscillations of the steady potential of the cerebral cortex. Nature. 1957;179(4567):957–9. https://doi.org/10.1038/179957a0.

    Article  Google Scholar 

  2. Ly JQM, Gaggioni G, Chellappa SL, Papachilleos S, Brzozowski A, Borsu C, Rosanova M, Sarasso S, Middleton B, Luxen A, Archer SN, Phillips C, Dijk D-J, Maquet P, Massimini M, Vandewalle G. Circadian regulation of human cortical excitability. Nat Commun. 2016;7(1):11828. https://doi.org/10.1038/ncomms11828.

    Article  Google Scholar 

  3. Vanhatalo S, Palva JM, Holmes MD, Miller JW, Voipio J, Kaila K. Infraslow oscillations modulate excitability and interictal epileptic activity in the human cortex during sleep. Proc Natl Acad Sci U S A. 2004;101(14):5053–7. https://doi.org/10.1073/pnas.0305375101.

    Article  Google Scholar 

  4. Monto S, Palva S, Voipio J, Palva JM. Very slow EEG fluctuations predict the dynamics of stimulus detection and oscillation amplitudes in humans. J Neurosci. 2008;28(33):8268–72. https://doi.org/10.1523/JNEUROSCI.1910-08.2008/.

    Article  Google Scholar 

  5. de Goede AA, van Putten MJAM. Infraslow activity as a potential modulator of corticomotor excitability. J Neurophysiol. 2019;122(1):325–35. https://doi.org/10.1152/jn.00663.2018.

    Article  Google Scholar 

  6. Sihn D, Kim S-P. Brain infraslow activity correlates with arousal levels. Front Neurosci. 2022;16:765585. https://doi.org/10.3389/fnins.2022.765585.

    Article  Google Scholar 

  7. Sihn D, Kim S-P. Differential modulation of behavior by infraslow activities of different brain regions. PeerJ. 2022;10:e12875. https://doi.org/10.7717/peerj.12875.

    Article  Google Scholar 

  8. Brinkman SL. Infra-slow electroencephalographic activity during stress. Master thesis. University of Twente. 2018. https://purl.utwente.nl/essays/77030.

  9. Sato N, Katori Y. (2019). Infra-slow electroencephalogram power associates with reaction time in simple discrimination tasks. Proceedings of the International Conference on Neural Information Processing; Sydney, NSW, Australia. https://doi.org/10.1007/978-3-030-36708-4_41.

  10. Zarenejad M, Yazdkhasti M, Rahimzadeh M, Tourzani ZM, Esmaelzadeh-Saeieh S. The effect of mindfulness-based stress reduction on maternal anxiety and self-efficacy: A randomized controlled trial. Brain Behav. 2020; 10(4):e01561. Doi: e01561.

  11. Kirk U, Axelsen JL. Heart rate variability is enhanced during mindfulness practice: a randomized controlled trial involving a 10-day online-based mindfulness intervention. PLoS ONE. 2020;15(12):e0243488. https://doi.org/10.1371/journal.pone.0243488.

    Article  Google Scholar 

  12. Ghawadra SF, Abdullah KL, Choo WY, Phang CK. Mindfulness-based stress reduction for psychological distress among nurses: a systematic review. J Clin Nurs. 2019;28(21–22):3747–58. https://doi.org/10.1111/jocn.14987.

    Article  Google Scholar 

  13. Gupta SS, Manthalkar RR, Gajre SS. Mindfulness intervention for improving cognitive abilities using EEG signal. Biomed Signal Process Control. 2021;70:103072. https://doi.org/10.1016/j.bspc.2021.103072.

    Article  Google Scholar 

  14. Lutz A, Slagter HA, Dunne JD, Davidson RJ. Attention regulation and monitoring in meditation. TRENDS COGN SCI. 2008;12(4):163–9. https://doi.org/10.1016/j.tics.2008.01.005.

    Article  Google Scholar 

  15. Van Dam NT, van Vugt MK, Vago DR, Schmalzl L, Saron CD, Olendzki A, Meissner T, Lazar SW, Kerr CE, Gorchov J, Fox KCR, Field BA, Britton WB, Brefczynski-Lewis JA, Meyer DE. Mind the hype: a critical evaluation and prescriptive agenda for Research on Mindfulness and Meditation. Perspect Psychol Sci. 2018;13(1):36–61. https://doi.org/10.1177/1745691617709589.

    Article  Google Scholar 

  16. Rodriguez-Larios J, Alaerts K. EEG alpha–theta dynamics during mind wandering in the context of breath focus meditation: an experience sampling approach with novice meditation practitioners. Eur J Neurosci. 2021;53:1855–68. https://doi.org/10.1111/ejn.15073.

    Article  Google Scholar 

  17. Rodriguez-Larios J, Bracho Montes de Oca EA, Alaerts K. The EEG spectral properties of meditation and mind wandering differ between experienced meditators and novices. NeuroImage. 2021;245:118669. https://doi.org/10.1016/j.neuroimage.2021.118669.

    Article  Google Scholar 

  18. Braboszcz C, Cahn BR, Levy J, Fernandez M, Delorme A. Increased gamma brainwave amplitude compared to control in three different meditation traditions. PLoS ONE. 2017;12(1):e0170647. https://doi.org/10.1371/journal.pone.0170647.

    Article  Google Scholar 

  19. Rodin E, Bornfleth H, Johnson M. DC-EEG recordings of mindfulness. Clin Neurophysiol. 2017;128(4):512–9. https://doi.org/10.1016/j.clinph.2016.12.031.

    Article  Google Scholar 

  20. Jo H-G, Naranjo JR, Hinterberger T, Winter U, Schmidt S. Phase synchrony in slow cortical potentials is decreased in both expert and trained novice meditators. Neurosci Lett. 2019;701:142–5. https://doi.org/10.1016/j.neulet.2019.02.035.

    Article  Google Scholar 

  21. Leong SL, Vanneste S, Lim J, Smith M, Manning P, De Ridder D. A XXXandomized, double-blind, placebo-controlled parallel trial of closed-loop infraslow brain training in food addiction. Sci Rep. 2018;8(1):11659. https://doi.org/10.1038/s41598-018-30181-7.

    Article  Google Scholar 

  22. Gabrielsen KB, Clausen T, Haugland SH, Hollup SA, Vederhus J-K. Infralow neurofeedback in the treatment of substance use disorders: a randomized controlled trial. J Psychiatry Neurosci. 2022;47(3):E222–9. https://doi.org/10.1503/jpn.210202.

    Article  Google Scholar 

  23. Mathew J, Adhia DB, Smith ML, De Ridder D, Mani R. Source localized infraslow neurofeedback training in people with chronic painful knee osteoarthritis: a randomized, double-blind, sham-controlled feasibility clinical trial. Front Neurosci. 2022;16:899772. https://doi.org/10.3389/fnins.2022.899772. PMID: 35968375; PMCID: PMC9366917.

    Article  Google Scholar 

  24. Grooms JK, Thompson GJ, Pan W-J, Billings J, Schumacher EH, Epstein CM, Keilholz SD. Infraslow electroencephalographic and dynamic resting state network activity. Brain Connect. 2017;7(5):265–80. https://doi.org/10.1089/brain.2017.0492.

    Article  Google Scholar 

  25. Keinänen T, Rytky S, Korhonen V, Huotari N, Nikkinen J, Tervonen O, Palva JM, Kiviniemi V. Fluctuations of the EEG-fMRI correlation reflect intrinsic strength of functional connectivity in default mode network. J Neurosci Res. 2018;96(10):1689–98. https://doi.org/10.1002/jnr.24257.

    Article  Google Scholar 

  26. Berkovich-Ohana A, Harel M, Hahamy A, Arieli A, Malach R. Alterations in task-induced activity and resting-state fluctuations in visual and DMN areas revealed in long-term meditators. NeuroImage. 2016;135:125–34. https://doi.org/10.1016/j.neuroimage.2016.04.024.

    Article  Google Scholar 

  27. Sauseng P, Klimesch W, Gerloff C, Hummel FC. Spontaneous locally restricted EEG alpha activity determines cortical excitability in the motor cortex. Neuropsychologia. 2009;47(1):284–8. https://doi.org/10.1016/j.neuropsychologia.2008.07.021.

    Article  Google Scholar 

  28. Thies M, Zrenner C, Ziemann U, Bergmann TO. Sensorimotor Mu-alpha power is positively related to corticospinal excitability. Brain Stimul. 2018;11(5):1119–22. https://doi.org/10.1016/j.brs.2018.06.006.

    Article  Google Scholar 

  29. Iemi L, Gwilliams L, Samaha J, Auksztulewicz R, Cycowicz YM, King J-R, Nikulin VV, Thesen T, Doyle W, Devinsky O, Schroeder CE, Melloni L, Haegens S. Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability. NeuroImage. 2022;247:118746. https://doi.org/10.1016/j.neuroimage.2021.118746.

    Article  Google Scholar 

  30. Delorme A, Braboszcz C. Meditation vs thinking task. OpenNeuro. [Dataset]. 2021. https://doi.org/10.18112/openneuro.ds003969.v1.0.0.

    Article  Google Scholar 

  31. Keren AS, Yuval-Greenberg S, Deouell LY. Saccadic spike potentials in gamma-band EEG: characterization, detection and suppression. NeuroImage. 2010;49(3):2248–63. https://doi.org/10.1016/j.neuroimage.2009.10.057.

    Article  Google Scholar 

  32. Muthukumaraswamy SD. High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations. Front Hum Neurosci. 2013;7:138. https://doi.org/10.3389/fnhum.2013.00138.

    Article  Google Scholar 

  33. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009.

    Article  Google Scholar 

  34. Kayser J, Tenke CE. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. evaluation with auditory oddball tasks. Clin Neurophysiol. 2006;117(2):348–68. https://doi.org/10.1016/j.clinph.2005.08.034.

    Article  Google Scholar 

  35. Kayser J, Tenke CE. Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: II. Adequacy of low-density estimates. Clin Neurophysiol. 2006;117(2):369–80. https://doi.org/10.1016/j.clinph.2005.08.03332.

    Article  Google Scholar 

  36. Kayser J. (2009). Current source density (CSD) interpolation using spherical splines - CSD toolbox (Version 1.1). New York State Psychiatric Institute: Division of Cognitive Neuroscience. http://psychophysiology.cpmc.columbia.edu/Software/CSDtoolbox.

  37. Tenke CE, Kayser J, Manna CG, Fekri S, Kroppmann CJ, Schaller JD, Alschuler DM, Stewart JW, McGrath PJ, Bruder GE. Current source density measures of electroencephalographic alpha predict antidepressant treatment response. Biol Psychiatry. 2011;70(4):388–94. https://doi.org/10.1016/j.biopsych.2011.02.016.

    Article  Google Scholar 

  38. Sihn D, Kim JS, Kwon O-S, Kim S-P. Breakdown of long-range spatial correlations of infraslow amplitude fluctuations of EEG oscillations in patients with current and past major depressive disorder. Front Psychiatry. 2023;14:1132996. https://doi.org/10.3389/fpsyt.2023.1132996.

    Article  Google Scholar 

  39. Tort ABL, Komorowski R, Eichenbaum H, Kopell N. Measuring phase-amplitude coupling between neuronal oscillations of different frequencies. J Neurophysiol. 2010;104(2):1195–210. https://doi.org/10.1152/jn.00106.2010.

    Article  Google Scholar 

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Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00213187 and No. RS-2023-00302489).

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Authors and Affiliations

  1. Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea

    Duho Sihn & Sung-Phil Kim

  2. School of Information Convergence, Kwangwoon University, Seoul, 01897, Republic of Korea

    Junsuk Kim

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Contributions

Duho Sihn: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing – Original Draft, Writing – Review & Editing, Visualization, Supervision, Project administration, Funding acquisition. Junsuk Kim: Writing – Original Draft, Writing – Review & Editing, Visualization, Supervision. Sung-Phil Kim: Writing – Original Draft, Writing – Review & Editing, Visualization, Supervision, Project administration, Funding acquisition.

Corresponding authors

Correspondence to Junsuk Kim or Sung-Phil Kim.

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Ethical approval

The authors conducted this study using the publicly available dataset of human subjects. This dataset is publicly available at: https://openneuro.org/datasets/ds003969/versions/1.0.0 [30]. Experiments on this dataset were approved by the ethical committee of the University of California San Diego (IRB project # 090731) [18].

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The authors have no relevant financial or non-financial interests to disclose.

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Sihn, D., Kim, J. & Kim, SP. Meditation-type specific reduction in infra-slow activity of electroencephalogram. Biomed. Eng. Lett. (2024). https://doi.org/10.1007/s13534-024-00377-0

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