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. 2019 Jun 12;13:178.
doi: 10.3389/fnhum.2019.00178. eCollection 2019.

The Human Default Consciousness and Its Disruption: Insights From an EEG Study of Buddhist Jhāna Meditation

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Free PMC article

The Human Default Consciousness and Its Disruption: Insights From an EEG Study of Buddhist Jhāna Meditation

Paul Dennison. Front Hum Neurosci. .
Free PMC article

Abstract

The "neural correlates of consciousness" (NCC) is a familiar topic in neuroscience, overlapping with research on the brain's "default mode network." Task-based studies of NCC by their nature recruit one part of the cortical network to study another, and are therefore both limited and compromised in what they can reveal about consciousness itself. The form of consciousness explored in such research, we term the human default consciousness (DCs), our everyday waking consciousness. In contrast, studies of anesthesia, coma, deep sleep, or some extreme pathological states such as epilepsy, reveal very different cortical activity; all of which states are essentially involuntary, and generally regarded as "unconscious." An exception to involuntary disruption of consciousness is Buddhist jhāna meditation, whose implicit aim is to intentionally withdraw from the default consciousness, to an inward-directed state of stillness referred to as jhāna consciousness, as a basis to develop insight. The default consciousness is sensorily-based, where information about, and our experience of, the outer world is evaluated against personal and organic needs and forms the basis of our ongoing self-experience. This view conforms both to Buddhist models, and to the emerging work on active inference and minimization of free energy in determining the network balance of the human default consciousness. This paper is a preliminary report on the first detailed EEG study of jhāna meditation, with findings radically different to studies of more familiar, less focused forms of meditation. While remaining highly alert and "present" in their subjective experience, a high proportion of subjects display "spindle" activity in their EEG, superficially similar to sleep spindles of stage 2 nREM sleep, while more-experienced subjects display high voltage slow-waves reminiscent, but significantly different, to the slow waves of deeper stage 4 nREM sleep, or even high-voltage delta coma. Some others show brief posterior spike-wave bursts, again similar, but with significant differences, to absence epilepsy. Some subjects also develop the ability to consciously evoke clonic seizure-like activity at will, under full control. We suggest that the remarkable nature of these observations reflects a profound disruption of the human DCs when the personal element is progressively withdrawn.

Keywords: EEG; active inference; consciousness; epilepsy; jhāna; meditation; slow-waves; spike-waves.

Figures

FIGURE 1
FIGURE 1
Three examples of spindling. The (Upper) panel is from a recording of subject 16, 2016; the (Middle) panel subject 1, 2015; and the (Lower) panel subject 7, 2015. Bandpass 5.3-15 Hz.
FIGURE 2
FIGURE 2
Meditation spindles compared to those in stage-2 nREM sleep for 27 independent recordings. The bar charts (Left) show spindle frequencies from an independent component (IC) spectral analysis in source space of 60-s segments from each recording. The data are split into two cohorts of meditation experience, 4–30 years and 30–44 years; the former yielding 16 spectral peaks and the latter 22 peaks, normalized to 20 for each cohort in the plot. At right are spindle density, duration and amplitude in sensor space from visual inspection of N = 30 consecutive spindles in each record (spindles at least 3x the inter-spindle background amplitude; total N = 810 spindles), compared to values typical of sleep. American Academy of Sleep Medicine guidelines (American Academy of Sleep Medicine [AASM], 2017) were followed for visual inspection.
FIGURE 3
FIGURE 3
Spindle sources computed using eLoreta for a 60-s sample of spindles (subject 14, 2016), bandwidth 5.3–15 Hz, 4-s epochs. The scalp mean spectral power distribution at upper-left shows maximum intensity in occipital regions, with some extension along the midline. The two strongest sources (ICs 1, 2) are limbic, at Brodmann sites B31 and B30, the cingulate and parahippocampal gyri respectively; with IC3 and IC4 at B7 (temporal) precunius, and B19 (occipital) the fusiform gyrus.
FIGURE 4
FIGURE 4
Extensive and powerful infraslow waves (ISWs) during Samatha meditation. (Top) panel subject 5, 2014; (Middle) panel subject 5, 2017; (Bottom) panel subject 17, 2016. The inset scalp intensity maps correspond to the start and end points of the yellow-highlighted intervals.
FIGURE 5
FIGURE 5
Extensive and powerful infraslow waves (ISWs). (Top) panel subject 24, 2016; (Middle) panel subject 24, 2017; (Bottom) panel subject 19, 2016. Note the posterior spike-wave bursts for subject 24, and the isolated extremely high voltage ISW shown by subject 19.
FIGURE 6
FIGURE 6
Infraslow-wave and spike-wave statistics in sensor space compared to sleep and absence epilepsy. The values for sleep and coma are from Sutter and Kaplan, 2012 and Libenson, 2012, and for spike waves from Sadleir et al., 2009.
FIGURE 7
FIGURE 7
Example of a travelling infraslow wave (subject 5, 2014), bandpass 0.016–150 Hz.
FIGURE 8
FIGURE 8
Top: summary of ISW regions of interest from Table 3 (7 independent recordings), with 3D cortical source plots. The fully developed vertex source is illustrated below for subject 5, 2017.
FIGURE 9
FIGURE 9
Evidence of a slower infraslow-wave (ISW) component: top panel, subject 17 (2016); middle panel subject 5 (2017). Below is a comparison of the highly focused vertex activity for subject 5 in 2014 and 2017, with the strongest ICs from 600-s samples shown for each year, with overall percentage contributions of ROIs from all 31 ICs for each year. The IC spectra alongside show higher frequency activity (bandwidth 5.3–150 Hz), revealing broadband gamma with only small residual traces of alpha activity.
FIGURE 10
FIGURE 10
Above are four examples of spike-wave bursts at occipital sites, using a bandwidth 0.53–70/150 Hz. Top to bottom are excerpts from an 8.6-s burst, subject 11, 2015; a 50-s burst, subject 26, 2016; an 8.3-s burst, subject 15, 2018; and a 3.025-s burst, subject 1, 2015. Below are the strongest ICs for subjects 1 and 7, from 3.025-s and 7.0-s bursts respectively, computed using eLoreta, showing harmonic spectral structure, spectral intensity distributions, and 3D source maps.
FIGURE 11
FIGURE 11
Examples of epileptiform activity. (A), subject 15, 2018; the top panel shows the main 70-s episode, with occipital activity expanded below; right is a source analysis of the 26-s segment (yellow-highlighted) from the main episode. (B), subject 7, 2015; the main 150-s episode is above, with the early part of the “seizure” (yellow-highlighted) expanded below, together with respiration trace.

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References

    1. American Academy of Sleep Medicine [AASM] (2017). The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. Available at: https://aasm.org/clinical-resources/scoring-manual/ (accessed April 20 2019).
    1. Barzegaran E., Vildavski V. Y., Knyazeva M. G. (2017). Fine structure of posterior alpha rhythm, in human EEG: frequency components, their cortical sources and temporal behaviour. Sci. Rep. 7:8249. 10.1038/s41598-017-08421-z - DOI - PMC - PubMed
    1. Bersagliere A., Pascual-Marqui R. D., Tarokh L., Achermann P. (2018). Mapping slow waves by EEG topography and source localization: effects of sleep deprivation. Brain Topogr. 31 257–269. 10.1007/s10548-017-0595-6 - DOI - PubMed
    1. Bizot F. (ed.) (1994). Recherches Nouvelles sur le Cambodge. Paris: Ecole Francaise d’Extreme-Orient; 101–127.
    1. Boly M., Garrido M. I., Gosseries O., Bruno M.-A., Boveroux P., Schnakers C., et al. (2011). Preserved feedforward but impaired top-down processes in the vegetative state. Science 332 858–862. 10.1126/science.1202043 - DOI - PubMed

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