Repertoire of mesoscopic cortical activity is not reduced during anesthesia

Abstract

Consciousness has been linked to the repertoire of brain states at various spatiotemporal scales. Anesthesia is thought to modify consciousness by altering information integration in cortical and thalamocortical circuits. At a mesoscopic scale, neuronal populations in the cortex form synchronized ensembles whose characteristics are presumably state-dependent but this has not been rigorously tested. In this study, spontaneous neuronal activity was recorded with 64-contact microelectrode arrays in primary visual cortex of chronically instrumented, unrestrained rats under stepwise decreasing levels of desflurane anesthesia (8%, 6%, 4%, and 2% inhaled concentrations) and wakefulness (0% concentration). Negative phases of the local field potentials formed compact, spatially contiguous activity patterns (CAPs) that were not due to chance. The number of CAPs was 120% higher in wakefulness and deep anesthesia associated with burst-suppression than at intermediate levels of consciousness. The frequency distribution of CAP sizes followed a power-law with slope -1.5 in relatively deep anesthesia (8-6%) but deviated from that at the lighter levels. Temporal variance and entropy of CAP sizes were lowest in wakefulness (76% and 24% lower at 0% than at 8% desflurane, respectively) but changed little during recovery of consciousness. CAPs categorized by K-means clustering were conserved at all anesthesia levels and wakefulness, although their proportion changed in a state-dependent manner. These observations yield new knowledge about the dynamic landscape of ongoing population activity in sensory cortex at graded levels of anesthesia. The repertoire of population activity and self-organized criticality at the mesoscopic scale do not appear to contribute to anesthetic suppression of consciousness, which may instead depend on large-scale effects, more subtle dynamic properties, or changes outside of primary sensory cortex.

Keywords: anesthesia; avalanche; consciousness; criticality; information integration; synchrony.

Fig. 1

An example of unit and LFP activity in rat visual cortex at four states of anesthesia and wakefulness in one rat. Spiking activity of approximately 100 neurons was recorded with a 64-site microelectrode array. In raster plot (top), each dot represents an extracellular spike. The number of spikes (Ns, below) in 10-ms bins from all neurons are plotted after 5-point weighted linear polynomial smoothing. The LFP trace is from data sampled at 100 Hz to match 10-ms bin spike counts. Binary nLFP events (bottom trace) are obtained at mean minus 1 standard deviation, red line shows this threshold. Desflurane concentration (%) is shown on top of each panel. The close temporal correlation between population spiking and nLFP is evident at all levels of anesthesia in spite of difference in overall spike rate and temporal pattern. Strongly intermittent unit activity in deep anesthesia (8%) is gradually transformed to nearly continuous activity en route to wakefulness. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Amplitude spectra of LFP from all recording sites and animals at five desflurane concentrations indicated in%. Individual data are shown in yellow (top panel). Peak amplitude as a function of desflurane (bottom panel). ^*p<0.05 vs. 8%, ^#p<0.05 vs. 8% and 6% (T-K).

Fig. 3

Distribution of CAP size in five states (anesthetic concentration indicated on top of each panel). CAP size s at each time point is included in the counts N(s). Data are pooled from twelve rats. Straight line (red) corresponds to power–law fit with slope alpha. The p value is from the K–S test. The number of large CAPs gradually drops below those predicted by power–law as the animals regain wakefulness. Shuffling the nLFP data diminishes CAPs larger than two and destroys the power–law (dotted line). Phase-randomization of LFP signal prior to thresholding does not change the power–law of CAP size distribution (dash-dotted line). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Effect of phase-randomization on nLFP correlations. (A) Steps of the analysis. The original signal (top left) is Fourier transformed to yield amplitude spectrum A(f) and phase spectrum F(t). Randomization of the phase at each frequency yields a new phase spectrum (bottom right) while the amplitude spectrum is conserved (middle right). The inverse Fourier transform yields a phase-randomized signal that looks different from the original (top right). The top panels show a short sample of the signal; spectra are from the entire 6-min segment. (B) Probability distribution of all pair-wise correlation coefficients of the LFP signals at five desflurane concentrations from the original and phase-randomized signals. (C) Average cross-correlation of nLFPs from all rats in each state. Original and phase-randomized data differ at 8% desflurane only. ^*p=0.00066.

Fig. 5

Summary statistics of CAPs (size >2) in rat visual cortex. (A) Total number of CAPs shows U-shape dependence on anesthetic depth. The number of CAPs is significantly elevated at 8% and 0% desflurane. ^*p < 0.05 vs. 6%, 4% and 2% (T-K). Separate, unconnected symbol at 8% shows data from four animals that show no burst-suppression. There is no significant difference among the intermediate levels. (B) Number of CAPs calculated from nLFPs thresholded at different standard deviation (2SD, 3SD) or sampled at different time increments (5 ms, 10 ms). The results are similar to those in panel A.

Fig. 6

Variation in the size s(t) of CAPs as a function of time across five conditions from 8% to 0% desflurane left to right (not indicated in figure). Vertical gray bars separate 4-min of data obtained in each condition. (The time scale is cumulative for the displayed data only; intervening periods are omitted.) Note the relatively large fluctuation of cluster size in rats that have burst-suppression at 8% desflurane (0–4 min, rats 1–8). The fluctuations are most suppressed during wakefulness (16–20 min).

Fig. 7

Dependence of CAP size variance (A) and entropy (B) on desflurane concentration. There is no difference near the recovery of consciousness, between 6% and 4% desflurane. ^*p<0.05 vs. all other levels combined.

Fig. 8

Typical landscapes of spatially correlated spontaneous population activity in rat visual cortex as indicated by local clusters (CAPs) of negative LFP deviations (nLFP). LFP data were recorded by a two-dimensional 8 − 8 multielectrode array. Data from five anesthetic conditions were concatenated and CAPs with size >2 at each time point were classified into 12 types by K-means clustering. Plots in each row show the average landscape of CAPs in 12 clusters arranged in descending order of the number of CAPs contained (percentage of CAPs shown on top of the panels). Pseudo-color corresponds to the probability of a site being a member of the corresponding CAP. The orientation of sites in each color plot is rostrocaudal (left–right) and dorsoventral (top-down). Each row is from a different rat.

Fig. 9

Results from CAP clustering as a function of state. (A) Number of CAP types (N(K)) present in each anesthetic condition at different cluster numbers K. K-means clustering was done on concatenated data and then the CAP types were counted in each condition. Virtually all cluster types occur in all states. (B) Number of CAPs N(K) in 12 clusters K. Anesthetic concentration is indicated in the legend. Data were combined from all animals, after ordering the 12 clusters according to the number of CAPs contained. Certain clusters contain disproportionally more CAPs at 8% and 0% desflurane than the rest. (C) Number of CAP centers that spatially cluster together at three smoothness thresholds. Clustering tendency is the highest in wakefulness but not different among the other states, particularly near the transition between unconsciousness (6%) and consciousness (4%).

Fig. 10

Center coordinates of CAPs in the visual cortex of twelve rats (arranged in the same order as in Fig. 4) in five states (anesthetic concentration indicated on top). Numbers by the axes indicate recording sites (X axis: rostrocaudal, Y axis: dorsoventral). CAP centers tend to segregate in different regions of the recording field that change with state in each animal.