EEG slow-wave coherence changes in propofol-induced general anesthesia: experiment and theory

Abstract

The electroencephalogram (EEG) patterns recorded during general anesthetic-induced coma are closely similar to those seen during slow-wave sleep, the deepest stage of natural sleep; both states show patterns dominated by large amplitude slow waves. Slow oscillations are believed to be important for memory consolidation during natural sleep. Tracking the emergence of slow-wave oscillations during transition to unconsciousness may help us to identify drug-induced alterations of the underlying brain state, and provide insight into the mechanisms of general anesthesia. Although cellular-based mechanisms have been proposed, the origin of the slow oscillation has not yet been unambiguously established. A recent theoretical study by Steyn-Ross et al. (2013) proposes that the slow oscillation is a network, rather than cellular phenomenon. Modeling anesthesia as a moderate reduction in gap-junction interneuronal coupling, they predict an unconscious state signposted by emergent low-frequency oscillations with chaotic dynamics in space and time. They suggest that anesthetic slow-waves arise from a competitive interaction between symmetry-breaking instabilities in space (Turing) and time (Hopf), modulated by gap-junction coupling strength. A significant prediction of their model is that EEG phase coherence will decrease as the cortex transits from Turing-Hopf balance (wake) to Hopf-dominated chaotic slow-waves (unconsciousness). Here, we investigate changes in phase coherence during induction of general anesthesia. After examining 128-channel EEG traces recorded from five volunteers undergoing propofol anesthesia, we report a significant drop in sub-delta band (0.05-1.5 Hz) slow-wave coherence between frontal, occipital, and frontal-occipital electrode pairs, with the most pronounced wake-vs.-unconscious coherence changes occurring at the frontal cortex.

Keywords: Turing–Hopf instabilities; gap-junction; mean-field cortical model; phase-coherence measure; slow-wave sleep.

Figures

Figure 1

Sample (A) wake (blue) and (B) sedated unconsciousness (red) EEG from archival Fp1 recording. Raw EEG data are filtered via AAR to remove eye-blink artifacts. AAR-corrected EEG are marked in black. The power spectra show that slow-wave oscillations are dominant in the sedated unconsciousness state.

Figure 2

Filtered Figure 1 EEG in sub-delta band (≤1.5 Hz) and corresponding power spectra (computed by Matlabfft) revealing a strong slow-wave (~0.3 Hz) in the sedated unconsciousness state.

Figure 3

Flowchart for processing EEG of two brain states to determine electrode-pairs with significantly altered phase-coherence. EEG data undergo preprocessing in EEGLAB before passing to EEG_coherence, a customized Matlab algorithm that automatically identifies electrode-pairs with significantly altered phase-coherence between two brain states across multiple subjects, then stores these electrode-pair results in a summary table.

Figure 4

Graphical representations of the electrode pairs with significantly reduced (upper panel) or increased (lower panel) phase-coherence of the sub-delta band (0.05–1.5 Hz) EEG induced by propofol anesthesia. EEG data (128-channel recording) were recorded from 5 subjects and processed by the EEG_coherence algorithm diagrammed in Figure 3. The electrode pairs with significant (p < 0.05) changes in phase coherence are connected with lines. The electrode-pair map is represented in a bird's-eye view of the 3D head model (created via the modified EEGLAB function plotchans3d). The black dots are EEG_coherence selected electrodes. Electrode pairs for altered phase coherence are determined with different levels of significance (significance-level p was set at 0.05, 0.025, and 0.01 in the Mann-Whitney U-test). Smaller p thresholds result in a lower density of electrode-pair cluster due to the stricter selection criterion, however, the electrode-pair distributions are generally preserved in trend.

Figure 5

A subset of electrode-pairs (left) showing significant (p < 0.05) reduction in phase coherence extracted from the upper left corner plot of Figure 4 (referenced to Cz, in dark blue lines) and Koskinen et al. reported pattern (Koskinen et al., 2001) (right, referenced to FCz, in light blue lines) for the coherence measured from 9 electrodes: Nz (nasion), Fp1′(about 1 cm down from Fp1, just above the eyebrow), Fp2′, Fz, F7, F8, Cz, Pz, and Oz.

Figure 6

Graphical representations of the electrode-pairs with significantly altered coherence from wake to coma for five subjects. The first and second rows represent electrode-pairs with significantly reduced (blue lines) or increased (pink lines) coherence, respectively: selected electrode-pairs correspond to the top 5% most changed (i.e., most increased or most decreased) coherence during the wake to coma transition. The third row describes the number difference of electrode-pairs between the first and second rows for four regions: frontal, occipital, left- and right-temporal; and for six pair-wise connections between regions: frontal–left temporal, frontal–right temporal, frontal–occipital, left temporal–occipital, right temporal–occipital, left–right temporal. The number of electrode-pairs with significantly reduced (or increased) coherence in a region is counted as N− (N+). The sign of (N− − N+) determines the dominance of a coherence trend: if (N− − N+) > 0, the region will be colored blue (decreased coherence); otherwise if (N− − N+) < 0, the region will be colored red (increased coherence). The (N− − N+) difference is calibrated by the color-gradient bar. (Note that the color-bar for the third row is not related to the first and second rows.)

Figure 7

Coherence matrices showing spatial distribution of electrode-pairs with significant wake vs. coma coherence difference. The coherence matrix is diagonally symmetric, we need only display its upper half. The first row of the left panel corresponds to the first row of Figure 6; the first row of the right panel corresponds to the second row of Figure 6. The marked (either in blue or pink) dots in the matrix are the top 5% most changed (decreased: blue; increased: pink) phase-coherence during transit from wake to coma. The row and column indices of a marked dot identify a pair of electrodes shown in Figure 6. To test the significance of the dot distribution in the first row, a permutation resampling is applied to each original matrix, and repeated 10,000 times. In each shuffling, the upper-triangle elements are randomly allocated, and a significance test is applied to achieve a p-value quantifying the structural difference between the permuted and original matrices. The first three permuted coherence matrices are shown. The averaged p-value over the 10,000 permutation tests for the original observations (first row) are all smaller than 10−5, revealing a significant difference between the original distribution and its permutations.

Figure 8

The steady-state firing rates Qoe as a function of varying anesthetic inhibition λi at a particular cortical excitation. The upper, high-firing and lower, low-firing branches (solid curve) are considered to be “awake” and “coma” states, respectively, with the “coma” state being associated with anesthetic-induced unconsciousness. Dashed curve indicates an unstable branch from which the cortex has the potential to jump to either the upper or lower stable branches. Upper and lower marked circles indicate references at λi = 1.0 and 1.018 on awake and coma branches, respectively. (Figure reproduced from Steyn-Ross et al., 2013).

Figure 9

At the λi = 1.0 wake state, cortical stability analysis and spatiotemporal dynamics for varying gap-junction strength D2 from 0.1 (top row) to 0.8 cm2 (bottom). Model cortex is initialized from the top high-firing branch of steady-state manifold marked as “Awake” in Figure 8. (A) Cortical stability analysis showing dominant eigenvalue dispersion curve of the real (black) and imaginary (red) parts as a function of scaled wavenumber for top- and bottom-branch equilibria at fixed anesthetic effect λi = 1.0 in Figure 8. Thus, each panel has two parts in it—the upper part corresponds to the top-branch, the lower part to the bottom-branch. The dotted line marks zero. (B) Last 4-s time-series of excitatory firing-rate Qe(t) extracted from 5 equally-spaced grid-points in (C)Qe(t, x) space-time chart representing the full 20-s time-evolution of cortical activity along the y = 60 midline strip; y-axis ranges from 0 to 30 s−1. (D) Bird's-eye snapshot Qe(y, x) of the cortex when t = 20 s. (E) Phase coherence map R(x′, x) showing synchronization level of firing-rate between Qe(t, x) and Qe(t, x′) for the final 5-s time evolution. The coherence level is computed via Hilbert transform Equation (3) with a transition from red to blue meaning high to low coherence. In (C–E), color scale from blue to red indicates the numerical range from low to high. (Figure modified from Steyn-Ross et al., 2013).

Figure 10

At the λi = 1.018 coma state, cortical stability analysis and spatiotemporal dynamics of varying gap-junction strength D2 from 0.1 (top row) to 0.9 cm2 (bottom). Model cortex is initialized from the bottom low-firing branch of steady-state manifold marked as “Coma” in Figure 8. (A) Cortical stability analysis showing dominant eigenvalue dispersion curve of the real (black) and imaginary (red) parts as a function of scaled wavenumber at anesthetic effect λi = 1.018 in Figure 8. (B) Last 4-s time-series of excitatory firing-rate Qe(t) extracted from 5 equal-spaced grid-points in (C)Qe(t, x) space-time chart representing the full 20-s time-evolution of cortical activity along the y = 60 midline strip. (D) Bird's-eye snapshot Qe(y, x) of the cortex when t = 20 s. (E) Phase coherence map R(x′, x) showing synchronization level of firing-rate between Qe(t, x) and Qe(t, x′) for the final 5-s time evolution. In (C–E), color scale from blue to red indicates the numerical range from low to high. (Figure modified from Steyn-Ross et al., 2013).

Figure 11

Global phase-coherence trends with respect to inhibitory strength for the cortex at (A) awake (λi = 1) and (B) comatose (λi = 1.018) states. Inhibitory strength D2 is evenly spaced (0.01 cm2 interval) in the range 0.0–1.0 cm2. At a given D2, simulations were repeated 10 times. For each simulation, we first computed the phase-coherence matrix R(x′, x) for the final 5-s time evolution (see Figures 9E, 10E), then extracted its upper-triangular matrix mean as an estimate of global phase-coherence, which is represented as a gray cycle in the figure. The trend curves were produced by spline function in Matlab curve-fitting toolbox. (Figure modified from Steyn-Ross et al., 2013).