Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness

Abstract

The neurophysiological mechanisms by which anesthetic drugs cause loss of consciousness are poorly understood. Anesthetic actions at the molecular, cellular, and systems levels have been studied in detail at steady states of deep general anesthesia. However, little is known about how anesthetics alter neural activity during the transition into unconsciousness. We recorded simultaneous multiscale neural activity from human cortex, including ensembles of single neurons, local field potentials, and intracranial electrocorticograms, during induction of general anesthesia. We analyzed local and global neuronal network changes that occurred simultaneously with loss of consciousness. We show that propofol-induced unconsciousness occurs within seconds of the abrupt onset of a slow (<1 Hz) oscillation in the local field potential. This oscillation marks a state in which cortical neurons maintain local patterns of network activity, but this activity is fragmented across both time and space. Local (<4 mm) neuronal populations maintain the millisecond-scale connectivity patterns observed in the awake state, and spike rates fluctuate and can reach baseline levels. However, neuronal spiking occurs only within a limited slow oscillation-phase window and is silent otherwise, fragmenting the time course of neural activity. Unexpectedly, we found that these slow oscillations occur asynchronously across cortex, disrupting functional connectivity between cortical areas. We conclude that the onset of slow oscillations is a neural correlate of propofol-induced loss of consciousness, marking a shift to cortical dynamics in which local neuronal networks remain intact but become functionally isolated in time and space.

Conflict of interest statement

Conflict of interest statement: E.N.B. and P.L.P have a patent pending on anesthesia monitoring.

Figures

Fig. 1.

Propofol induction of unconsciousness causes a sharp drop and slow recovery of spike rates. Arrows indicate approximate times of propofol administration (±20 s). (A) Sample spike raster from patient A. Units are sorted by post-LOC spike rate. Red line indicates time of LOC. A second drop in spike rate is visible after a second propofol bolus. (B) Bayesian state-space estimate of population spike rate by patient, locked to LOC (vertical black line). Population spike rate is normalized to pre-LOC period. Shaded region shows 95% confidence intervals. All spike rates drop within 0–30 s of LOC and then begin rising ∼1 min after LOC (the second drop in patient A occurred after a second propofol bolus).

Fig. 2.

The slow oscillation develops abruptly at LOC and is maintained thereafter. Dashed black line indicates LOC in both panels. (A) Slow oscillation (0.1–1 Hz) power in the LFP from a representative microelectrode across time, where time is computed relative to LOC. Slow oscillation power increases sharply at LOC in all patients and remains higher than baseline throughout the post-LOC period. (B) Zoomed-in spectrogram from a representative microelectrode in patient B, where power is normalized within each frequency band to the pre-LOC period. The abrupt and stable power increase after LOC is specific to the slow oscillation band.

Fig. 3.

Spikes become phase-coupled to the slow oscillation at LOC. Dashed line indicates LOC in both panels. (A) Sample LFP from a representative microelectrode in patient B. Filtered slow oscillation is overlaid in red, and the mean spike rate across all units is in black, showing onset of slow oscillation at LOC. The LOC period is shaded in light green and the post-LOC period in darker green. (B) (Left) Phase-coupling of all single units in patient B to their local LFP slow oscillation, where color indicates the percentage of spikes in a given phase bin. Plot demonstrates that phase-coupling begins at LOC. (Right) Red line shows a sinusoid to indicate slow oscillation phase. Histogram shows the phase distribution of all post-LOC spikes, which are coupled to the rising phase of the slow oscillation. See Fig. S2 for identical analysis of patients A and C.

Fig. 4.

Slow oscillations in distant ECoG channels have variable phase offsets. The PLF characterizes the stability of the phase offset between two oscillations over a period, selected as either pre- or post-LOC. The PLF magnitude ranges between 0 and 1, where 1 reflects constant phase offset, and 0 represents variable phase offset. The PLF angle indicates the average phase offset. (A) PLF magnitude between each pair of ECoG electrodes, with pre-LOC PLF on the x-axis and post-LOC PLF on the y-axis. Pre- and post-LOC PLF magnitude are highly correlated. The red line marks the line of best fit to the data. (B) PLF magnitude during the post-LOC recording, plotted according to the distance between the electrodes in each pair. The PLF magnitude decreases significantly with distance, reflecting higher variability in phase offsets between distant ECoG electrodes. Red lines show mean (± SD) PLF magnitude over all electrode pairs at that distance. (C) 2D histogram of the PLF angle between all ECoG pairs after LOC, showing that the mean phase offset is more variable between distant channels, with values as large as π, than between nearby channels.

Fig. 5.

After LOC, slow oscillations are asynchronous across cortex and are associated with ON/OFF states; therefore, distant cortical areas frequently are at a suppressed phase during local ON periods. (A) Position of ECoG and microelectrode recordings in patient B. Each white circle marks the location of an ECoG electrode, and the microelectrode where spikes were recorded is marked with a star. (B) Phase histograms for every single unit in patient B. Each panel displays the spike coupling to phase in a different recording site. Units are arranged by post-LOC spike rate with the highest rate at top of plot, and the same phase-coupling trend is visible across all units to the phase of the LFP and nearby ECoG (gr13). In contrast, the slow oscillation in the distant ECoG channel (gr28) does not have the same phase relationship to local spiking. (C) Magnitude of the PLF between every ECoG electrode relative to the ECoG closest to the spike recordings (gr13). The PLF drops with distance in both the pre- and post-LOC states, showing that distant areas have variable phase offsets relative to the local recording. (D) MI quantifying the strength of the spike–phase relationship. The MI is consistently low in the pre-LOC state, demonstrating the absence of a strong spike–phase relationship. After LOC, the MI is high only in local ECoG recordings, demonstrating that spikes are strongly phase-coupled to local slow oscillations and that this relationship weakens with distance. (E) Traces from a nearby ECoG (blue), distant ECoG (green), representative LFP channel (red), and mean spike rate across all units (black). Arrows mark the times that the ECoG slow oscillation is at phase zero (when local spike rates are expected to be high; see Fig. 3). Gray shading marks the times at which the LFP slow oscillation phase is between −π/4 and 3π/4, when most spiking occurs. Plots show that, after LOC, local spikes occur in ON periods that typically overlap with the zero phase in the nearby ECoG channel but frequently do not overlap with the zero phase in the distant ECoG channel.

Fig. 6.

Spikes occur in brief ON periods that maintain interunit structure. (A) Parameter estimates from the best GLM model for population spiking after LOC. For each patient, the best model includes information from both LFP phase and recent population spike history. (B) Time-series example. Units 11 and 78 spike together; unit 32 has a similar spike rate but does not. These units preserve the same correlation structure after LOC that they had before LOC. (C) Example of cross-intensity functions [square-root estimate (51)] before and after LOC, showing that the pre-LOC interunit structure persists after LOC.

Fig. 7.

Spike activity is associated with modulations in slow oscillation morphology and gamma power. (A) LFP spectrogram triggered at onset of ON periods (black line). Power is normalized within each band and log-transformed. (B) Population spike histograms for each patient, demonstrating spike activity locked to ON period detection times. (C) LFP average time-series triggered at ON period onset times, showing an increased LFP peak after spike activity compared with before spike activity. Shaded region indicates approximate 95% confidence intervals. The slow oscillation peak is significantly higher after spiking than before spiking. (D) LFP triggered at troughs of the slow oscillation. Purple trials are cycles of the slow oscillation associated with many spikes, and brown trials are cycles that were not associated with any detected spikes (n = 153 cycles per condition, drawn from all three patients). Only trials associated with high spike rates show an asymmetric LFP peak. The shaded region indicates approximate 95% confidence intervals. (E) Power spectra of LFP time-series triggered at the trough of the slow oscillation. Gray region indicates significant difference in >10 Hz power during trials with high spike rates.

Fig. P1.

Propofol-induced loss of consciousness occurs at the onset of a slow oscillation that is associated with fragmented neuronal networks. (A) Loss of consciousness occurs simultaneously with the onset of the slow oscillation. Unconsciousness is not associated with consistent changes in mean spike rates. Instead, spiking is grouped into short windows locked to the trough of the slow (<1 Hz) oscillation. These troughs occur at different times in different cortical regions, thereby fragmenting activity in different brain regions into distinct, asynchronous windows. LFP, local field potential. (B) Slow oscillation marks a state in which functional connectivity can be preserved within small (<4 mm) neuronal networks; however, processing within a local network is interrupted periodically by suppression of spiking. Global communication between brain regions also is impaired, because these suppressions occur at different times across cortical areas.