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Fractals, Vol. 19, No. 1 (2011) 113–123c© World Scientific Publishing CompanyDOI: 10.1142/S0218348X1100521X

DIFFERENT ANESTHESIA IN RATINDUCES DISTINCT INTER-STRUCTURE

BRAIN DYNAMIC DETECTED BY HIGUCHIFRACTAL DIMENSION

SLADJANA SPASIC,∗,‡ SRDJAN KESIC,† ALEKSANDAR KALAUZI∗and JASNA SAPONJIC†,§

∗Department for Life Sciences, Institute for Multidisciplinary ResearchUniversity of Belgrade, Kneza Viseslava 1, 11 030 Belgrade, Serbia

†Department of NeurobiologyInstitute for Biological Research–Sinisa StankovicUniversity of Belgrade, Despot Stefan Blvd. 142

11060 Belgrade, Serbia‡sladjana@imsi.rs; sladjana@ibiss.bg.ac.rs

§jasnasap@ibiss.bg.ac.rs; jasnasaponjic@yahoo.com

Received August 25, 2009Revised May 12, 2010

Accepted November 16, 2010

AbstractThe complexity, entropy and other non-linear measures of the electroencephalogram (EEG),such as Higuchi fractal dimension (FD), have been recently proposed as the measures of anes-thesia depth and sedation. We hypothesized that during unconciousness in rats induced by thegeneral anesthetics with opposite mechanism of action, behaviorally and poligraphically con-trolled as appropriately achieved stable anesthesia, we can detect distinct inter-structure braindynamic using mean FDs. We used the surrogate data test for nonlinearity in order to establishthe existence of nonlinear dynamics, and to justify the use of FD as a nonlinear measure in the

‡,§Corresponding authors.

113

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114 S. Spasic et al.

time series analysis. The surrogate data of predefined probability distribution and autocorrela-tion properties have been generated using the algorithm of statically transformed autoregressiveprocess (STAP). FD then is applied to quantify EEG signal complexity at the cortical, hip-pocampal and pontine level during stable general anesthesia (ketamine/xylazine or nembutalanesthesia).

Our study showed for the first time that global neuronal inhibition caused by differentmechanisms of anesthetic action induced distinct brain inter-structure complexity gradient inSprague Dawley rats. EEG signal complexities were higher at cortical and hippocampal level inketamine/xylazine vs. nembutal anesthesia, with the dominance of hippocampal complexity. Innembutal anesthesia the complexity dominance moved to pontine level, and ponto-hippocampo-cortical decreasing complexity gradient was established. This study has proved the Higuchifractal dimension as a valuable tool for measuring the anesthesia induced inter-structure EEGcomplexity.

Keywords : Higuchi Fractal Dimension; Nonlinearity in EEG; Surrogate Data;Ketamine/Xylazine; Nembutal; Anesthesia.

1. INTRODUCTION

During last decade, several nonlinear measuressuch as nonlinear correlation index, Higuchi frac-tal dimension (FD), Shannon entropy, approximateentropy and Lempel-Ziv complexity, have been suc-cessfully introduced in EEG analysis to investi-gate neuronal dynamic behavior.1–4 Investigationof the brain dynamics by nonlinear analysis of theEEG signals is used to define the depth of seda-tion under anesthesia,5,6 to compare the proprietiesof the specific spontaneous or anesthesia inducedEEG waveforms,7 and to discriminate ictal (seizure)from pre-ictal (before seizure) brain activity or crit-ical brain site for seizure.8 The nonlinear measuresalso defined the insomnia subtypes on the bases ofcharacterized daytime cortical activation in normalsleepers versus chronic insomniacs.9 Since actualliving biological systems are not stable, and thesystem complexity varies with time, it is possibleto distinguish different states of the system by thefractal dimension, or to define different complexitybetween the structures within the same biologicalsystem such as the brain.8,10,11

In this study, by using Higuchi fractal dimen-sion, as a measure of the EEG signal complexity,we compared the brain complexity at the distinctbrain levels (sensorimotor cortex, hippocampus andpons), during stable anesthesia induced by dif-ferent anesthetics (ketamine/xylazine or nembutalanesthesia), and appropriately achieved for oper-ative and experimental procedure in rat. Stabil-ity of anesthesia were estimated on the bases ofthe observed loss of consciousness, muscle atonia,

absence of the tail-pinch, ear-pinch (analgesia) andcorneal reflexes, and on the bases of poligraphicrecording during experimental procedure, includingregular breathing pattern.12,13

Two main objectives underlie this study. First,we compared the EEG signal complexities underketamine/xylazine or nembutal anesthesia at differ-ent brain levels (cortical, hippocampal and pontineEEGs) using Higuchi fractal dimension. Second, theEEG complexities within the distinct brain struc-tures were compared in ketamine versus nembutalanesthesia.

We hypothesized that it is possible to detect dif-ferent complexity of the EEG signals at differentlevels within the rat brain during stable anesthe-sia, caused by two anesthetics with opposite mech-anisms of action (ketamine/xylazine or nembutalanesthesia).

2. MATERIALS AND METHODS

2.1. EEG Data: ExperimentalAnimals, Operative Procedure,Data Recording andAcquisition

Experiments were performed in 17 male SpragueDawley rats, weighing 200–300 g prior to surgery,maintained on a 12 h light-dark cycle, and housedat 25◦ C with free access to food and water. Prin-ciples for the care and use of laboratory animals inresearch were strictly followed, as outlined by theGuide for the Care and use of Laboratory Animals

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Brain Structure Complexity Patterns in Anesthesia 115

(National Academy of Sciences Press, Washington,DC, 1996).

Rats were anesthetized with a combination of80 mg/kg ketamine (Abbott Laboratories, NorthChicago, IL), and 5 mg/kg xylazine (Phoenix Scien-tific Inc. St Joseph, MO) (8/17) or with 50 mg/kgnembutal (9/17) given by intraperitoneal injection.After a surgical plane of anesthesia was achieved(controlled by absence of the tail- or ear-pinch andcorneal reflexes), rats were placed in the stereotaxicapparatus (David Kopf Inst., model 962 A Tujunga,CA). Following the electrodes implantation, bilat-eral referential EEGs were recorded using stainlessscrews in the parietal cortex (P: 2.5; R/L: 2; V:1 to bregma), and teflon coated wires (Medwire,MT. Vernon, NY) in the CA1 region of hippocam-pus (P: 3.8; R/L: 1.7; V: 2.7 to bregma). A bipo-lar twisted wire electrode with uninsulated tips of1 mm was stereotaxically targeted into right pedun-culopontine tegmental nucleus (PPT) to record thepontine EEG (P: 8; R: 1.8; V: 7 to bregma).

During each experimental protocol, we performedan 8-channel recording: (1) respiratory movementsmonitored by a piezoelectric strain gauge (Infant-Ped Sleepmate Technologies, Mislothian, VA); (2)an injection marker signal obtained from MPPI-2;(3) EEG from left sensorimotor cortex; (4) EEGfrom right sensorimotor cortex; (5) left hippocam-pal EEG; (6) right hippocampal EEG; (7) right pon-tine EEG; and (8) genioglossal EMG. The details ofthis method are more fully explained elsewhere.12,13

After conventional amplification and filtering (0.1–50 Hz band-pass; Grass Model 12, West Warwick)the analog data were digitized (sampling frequency100/s) and recorded using Brain Wave for Windowssoftware (Datawave Systems, Longmont, CO).

In this study we used 180 s of all EEG recordsduring stable control anesthetized condition: (1)EEG from left sensorimotor cortex; (2) EEG fromright sensorimotor cortex; (3) left hippocampalEEG; (4) right hippocampal EEG; and (5) rightpontine EEG.

2.2. STAP Algorithm

The application of nonlinear methods in time seriesanalysis, like fractal or other analysis has to bejustified by establishing nonlinearity in the timeseries.14 We have used the algorithm of staticallytransformed autoregressive process (STAP) to gen-erate the surrogate data with detailed description

in Kugiumtzis, 2002.15 For the given time series, wehave generated the surrogate data sets, and calcu-lated the Higuchi’s fractal dimension for the originaland surrogate data. Finally, we have tested wherethe FD of the data lies in the context of the sur-rogate results. When the original data yields theFD statistical significant difference from FDs of thesurrogate data sets, we can have more confidencethat original EEG signals arises from a nonlinearprocess. STAP algorithm generates the surrogatedata as realizations of a suitable statically trans-formed autoregressive process. It uses a typical real-izations approach and attempts to build a properautoregressive (AR) model in order to generate datathat match two conditions. The first, surrogate timeseries preserves exactly the marginal probabilitydensity function of the original time series and thesecond — the STAP algorithm finds analyticallythe autocorrelation of the surrogate and originaltime series for sufficient range of lags τ . The STAPalgorithm is applied using MATLAB software byKugiumtzis. We used STAP software with follow-ing free parameters: m = 5 as the degree of thepolynomial to approximate the sample transform,p = 50 as an order of the AR(p) model to generatethe Gaussian time series, M = 50 is a number ofsurrogates to be generated, τmax = p = 50 is maxi-mum lag to be used in the comparison of autocorre-lations, and K = M = 50 is a number of repetitionsto be made to find the “best” AR model.

2.3. Higuchi Fractal Dimension

Fractal analysis was performed by estimating frac-tal dimension values of the cerebral cortical, hip-pocampal and pontine EEG signals using Higuchi’salgorithm.16 Fractal dimension is a nonlinear mea-sure of signal complexity in time domain. It isvery simple and useful method for assessmentnonlinearity of signal without reconstruction astrange attractor.17 Briefly, EEG was analyzedin time sequences x(1), x(2), . . . , x(N) and it wasconstructed in a new self-similar time seriesXm

k as:

Xmk : x(m), x(m + k),

x(m + 2k), . . . , x(m + int[(N − k)/k]k)

for m = 1, 2, . . . , k where m is initial time; k =2, . . . , kmax, where k is time interval, kmax is a freeparameter, and int(r) is integer part of the real

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116 S. Spasic et al.

number r. The length Lm(k) was computed for eachof the k time series or curves Xm

k .

Lm(k) =1k

int[N−mk

]∑i=1

|x(m + ik)

−x(m + (i − 1)k)|

N − 1

int[

N−mk

]k

,

where N is the length of the original time series Xand (N − 1)/{int[(N − m)/k]k} is a normalizationfactor. Lm(k) was averaged for all m forming themean value of the curve length L(k) for each k =2, . . . , kmax as

L(k) =∑k

m=1 Lm(k)k

.

An array of mean values L(k) was obtained and theFD was estimated as the slope of least squares linearbest fit from the plot of ln(L(k)) versus ln(1/k):

FD = ln(L(k))/ ln(1/k).

After preliminary tests, our results confirm that theFD is independent of the length of the window, atleast for N > 100, but much more dependent ofthe parameter kmax. So, we chose the parameterN = 500 equivalent to an epoch of 5 s duration andthe parameter kmax = 8. We used the parameterkmax = 8 on the basis of our former study18 of theoptimum choice for kmax value. EEG signals weredivided into 36 epochs (windows). FD values werecalculated for each epoch, without overlap. Indi-vidual FD values were averaged across all epochsfor the signal. Higuchi’s algorithm is applied usingMATLAB software that has been validated in ourprevious publications.10,11,18,19

In order to facilitate the interpretation of FD val-ues of the EEG signals, the FD value of smoothcurve (for example linear or a low frequency sinewave) was estimated to be ∼1. The FD of randomwhite noise was estimated to be ∼2.

2.4. Fourier Amplitude Spectra

We also calculated Fourier amplitude spectra oneach 5 s epoch during overall 180 s of stable con-trol anesthetized condition for each animal and forall five recorded structures as described before. Foreach type of anesthesia and each recorded struc-ture we averaged normalized amplitude spectra (fornembutal anesthesia N = 9, for ketamine/xylazineanesthesia N = 8).

2.5. Statistical Test for Nonlinearity

A general null hypothesis is that the signal is astatic nonlinear transformation s(xn) of a linearGaussian process xn. This hypothesis is tested bygenerating surrogate data. We have generated anensemble of 50 surrogate data sets for every cere-bral, hippocampal and pontine EEG signal in orderto test the null hypothesis H0 that the originalrat biosignals are generated by linear stochasticprocess undergoing static nonlinear transform s.The surrogate data must have the same autocor-relation, and the same amplitude distribution asthe original EEG signals to represent H0. The nullhypothesis H0 is rejected at the 0.05 significancelevel, under the assumption of Gaussian distribu-tion of statistic q, if the discriminating statisticq0, from an applied nonlinear method on the origi-nal data set, is statistically different from statisticsq1, . . . , qM of the M surrogates, for range of the freeparameters (τ and m) for each statistic. As a dis-criminating statistics, we used the Higuchi fractaldimension.

To compare the original and the surrogate series,discriminative statistics, FD are calculated for bothseries. We used one sample two-tail t-test. Thestatistics of significance S is

S(i) =|FD0(i) − FDs(i)|

σFD(i),

where FD0(i) stands for the statistic on the origi-nal time series, FDs(i) stands for the average of thestatistic on the surrogate time series, and αFD(i)is the standard deviation of the statistic on thesurrogate time series, for each original signal i =1, 2, . . . , 85. For α = 0.05, the critical value of Sis 1.96. Therefore, the null hypothesis was rejectedwhen S > 1.96, at the 0.05 significance level andthe original signal was considered as nonlinear.

2.6. Statistical Analysis of Data

We tested the differences between the Higuchi frac-tal dimensions of EEG signals from the certainbrain structures of ketamine/xylazine or nembutalanesthetized rats using one-factor Fridman ANOVAwith post-hoc Wilcoxon Matched Pairs Test. Thedifferences between fractal dimension values of thebrain structures between groups (ketamin/xylazinevs. nembutal anesthetized rats) we tested by one-factor Kruskal-Wallis ANOVA and post-hoc Mann-Whitney U Test.

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3. RESULTS

We have calculated Higuchi fractal dimension ofthe sensorimotor cortical, hippocampal and pon-tine EEG signals in different experimental condi-tions: in ketamine/xylazine or nembutal anesthesia.Higuchi fractal dimension values were calculated forall (40 + 45) signals in (8 + 9) rats, during stablecontrol anesthetized condition.

3.1. Surrogate Data Analysis

The evidences of nonlinearity (Fig. 1), with FD asdiscriminative statistics, were found in 7/8 rats inleft cerebral cortical EEG, 8/8 rats in right cerebralcortical EEG, 5/8 rats in left hippocampal EEG,7/8 rats in right hippocampal EEG, and 6/8 ratsin pontine EEG in ketamine/xylazine anesthesia.The evidences of nonlinearity were found in 8/9rats in left cerebral cortical EEG, 8/9 rats in rightcerebral cortical EEG, 5/9 rats in left hippocam-pus, 8/9 rats in right hippocampus, and in 8/9 ratsin pontine EEG in nembutal anesthesia. Our nullhypothesis that the EEG series are generated bythe statically transformed autoregressive process,AR(50), has been rejected in 73.68% cases of cere-bral, hippocampal, and pontine EEG. Of course, thefact that the null hypothesis can not be rejected onthe basis of the given evidence does not prove thatthe dynamic originated from statically transformedautoregressive process.

3.2. Structure Related ComplexityUnder Ketamine/XylazineAnesthesia

We evaluated intrastructural complexity of the ratbrain under ketamine/xylazine anesthesia on the

Fig. 1 Testing hypothesis of nonlinearity by Higuchi fractaldimension as statistics. Results are depicted in percentages.

bases of the mean FD values (Fig. 2(c), Table 1).Friedman ANOVA showed significant structurerelated complexity difference (ChiSqr = 12.50,N = 8, df = 4, p < 0.014).

The highest mean FD value is in the left hip-pocampus (Fig. 2(c)). The hippocampal complexityis higher than cortical complexity on the same sideof the brain (Figs. 2(a)–2(c); z ≤ 2.24; p ≤ 0.05).Complexity of the left hippocampus is higher thanpontine complexity (z = 2.38; p = 0.02), and mean

(a)

(b)

(c)

Fig. 2 Different brain inter-structure complexity gradi-ent under ketamine/xylazine anesthesia ((b) left brain;(a) right brain) in Sprague-Dawley rats, and the meanvalue and standard deviation of the Higuchi fractal dimen-sions (mean FD and sd FD) of each brain structure inketamine/xylazine anesthesia (c) Cx – cortex; Hipp – hip-pocampus, PPT – pedunculopontine nucleus within the pons.Solid arrow – statistically significant order of complexity;dashed arrow – tendency of complexity order.

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118 S. Spasic et al.

Table 1 Results of Wilcoxon Matched PairsTests of the Interstructure FDs Differences UnderKetamine/Xylazine Anesthesia. Bold p-Values areSignificant.

p-Level Cx Right Hipp Left Hipp Right Pons

Cx left 0.40 0.03 0.12 0.48Cx right 0.03 0.05 0.33Hip left 0.07 0.02Hip right 0.07

FD of the left hippocampus shows a tendency tobe higher than the right hippocampal FD (z =1.82; p = 0.07), while FD of the right hippocam-pus shows a tendency to be higher than pontine

(a) (b)

(c) (d)

Fig. 3 Averaged normalized amplitude spectra of 180 s of EEG under ketamine ((a) left side, (b) right side; N = 8), andnembutal ((c) left side, (d) right side; N = 9) anesthesia including pontine (dotted line), hippocampal (dashed line), andcortical (black-solid line) EEG spectra that we analyzed using nonlinear measure of complexity — Higuchi fractal dimension.

FD (Fig. 2(b); Table 1; z = 1.82; p = 0.07). Thereis no difference between FDs of the left and rightcortex (z = 0.84; p = 0.40). Pontine complexity isnot different than complexity of both cortical sides(z ≤ 0.98; p ≤ 0.48).

Table 1 indicates the p-values calculated pairwisefor the mean FDs of the EEG signals: cortex left,cortex right, hippocampus left, hippocampus right,and pons right.

Figures 3(a) and 3(b) depicts the correspond-ing averaged (N = 8) normalized EEG spectraof 180 s of stable control condition per each struc-ture within the same side of the brain (right orleft brain side) during ketamine/xylazine anesthesia

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Brain Structure Complexity Patterns in Anesthesia 119

that we analyzed using nonlinear measure of com-plexity — Higuchi fractal dimension. The charac-teristic 5–10 Hz frequency activity could be noticedon both brain sides in each structure.

3.3. Structure Related ComplexityUnder Nembutal Anesthesia

Friedman ANOVA showed significant structurerelated complexity difference between the brain

(a)

(b)

(c)

Fig. 4 Different brain inter-structure complexity gradientunder nembutal anesthesia ((a) left brain; (b) right brain)in Sprague-Dawley rats, and the mean value and standarddeviation of the Higuchi fractal dimensions (mean FD andsd FD) of each brain structure (c) Cx – cortex; Hipp – hip-pocampus, PPT – pedunculopontine nucleus within the pons.Solid arrow – statistically significant order of complexity.Characteristic ponto-hippocampo-cortical complexity gradi-ent established within the right brain side is depicted (b).

structures of nembutal anesthetized rats(Chi Sqr = 19.60, N = 9, df = 4, p < 6 × 10−4).Figure 4(c) depicts that the left hippocampal com-plexity was the highest. Like in ketamine/xylazineanesthesia, the hippocampal complexity is higherthan cortical complexity of the same brain side(z ≤ 2.67; p ≤ 0.40), and there is no differencebetween complexity of the left and right cortex(z = 1.60; p = 0.11). Also, left hippocamal com-plexity is higher than pontine complexity (Figs. 4(a)and 4(c); z = 2.19; p = 0.03). In contrast toketamine anesthesia, the pontine EEG signal com-plexity (Figs. 4(a) and 4(b)) was higher than thecortical signal complexities (z = 2.55; p = 0.01),and right hippocampal complexity (z = 1.95; p =0.05). Although mean FD of the left hippocampusis the highest like in ketamine anesthesia, in thenembutal anesthetized rats the ponto–hippocampo-cortical complexity gradient was established withinthe right brain — the highest complexity in the ponswith decrement to cortical level (Fig. 4(b)).

Results of Wilcoxon Matched Pairs Test areshown in (Table 2), and the p-value calculated pair-wise for the EEG signals are depicted: cortex left,cortex right, hippocampus left, hippocampus right,and pons right.

The corresponding averaged (N = 9) normal-ized EEG spectra of 180 s of stable control con-dition per each structure within the same side ofthe brain (right or left brain side) during nembutalanesthesia that we analyzed using FD are depictedon Figs. 3(c) and 3(d).

3.4. Structure Related BrainComplexities Differences inKetamine/Xylazine vs.Nembutal Anesthesia

We evaluated complexity of the rat brain underketamine vs. nembutal anesthesia by Kruskal-WallisANOVA of independent grouping variables. Thereis a significant difference between complexity of the

Table 2 Results of Wilcoxon Matched Pairs Test ofthe Interstructure FDs Differences Under NembutalAnesthesia. Bold p-Values are Significant.

p-Level Cx Right Hipp Left Hipp Right Pons

Cx left 0.11 0.01 0.44 0.01Cx right 0.01 0.04 0.01Hip left 0.02 0.03Hip right 0.05

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120 S. Spasic et al.

(a)

(b)

(c)

Fig. 5 Comparison of the inter-structure brain complex-ity in ketamine vs. nembutal anesthesia ((a) left brain;(b) right brain) in Sprague-Dawley rats, and the mean valuethe Higuchi fractal dimensions (mean FD) of each brain struc-ture (c) Cx – cortex; Hipp – hippocampus, PPT – peduncu-lopontine nucleus within the pons. ** p ≤ 0.01.

brain structures in ketamine/xylazine versus nem-butal anesthetized rats (H(9, N = 85) = 44.43; p <10−5). Mann-Whitney U Test showed (Fig. 5(c))that cortical complexity of the left (Fig. 5(a); z =3.08; p < 9 × 10−4) as well as of the right side(Fig. 5(b); z = 2.60; p < 7 × 10−3) was signif-icantly higher in ketamine vs. nembutal anesthe-sia. Right hippocampal complexity was significantlyhigher (z = 2.60; p < 7×10−3) in ketamine anesthe-sia. However, there is no statistically significant dif-ferences between the left hipocampal complexities

Table 3 Mean FD Values and CorrespondingStandard Deviations (sd FD) of Each Brain Struc-ture in Ketamine/Xylazine and Nembutal Anes-thesia.

Brain Structure Mean FD ± sd FD

Ketamine/Xylazine Nembutal

Cx left 1.39 ± 0.05 1.32 ± 0.03Cx right 1.38 ± 0.05 1.31 ± 0.05Hipp left 1.52 ± 0.10 1.43 ± 0.09Hipp right 1.45 ± 0.04 1.35 ± 0.08Pons 1.40 ± 0.06 1.38 ± 0.06

(z = 1.54; p = 0.14), as well as in pons (z =1.15; p = 0.28) in different anesthesias.

All mean FD values and corresponding standarddeviations (sd FD) calculated for EEG signals ofdifferent brain structures and for both anesthetizedconditions are shown in (Table 3).

4. DISCUSSION

We have shown for the first time that at the samelevel of anesthesia induced unconsciousness estab-lished by distinct anesthetics (ketamin/xylazine ornembutal) in Sprague-Dawley rats, with appro-priately achieved analgesia, atonia, areflexia andregular breathing pattern, there was still a signif-icantly different brain inter-structural EEG com-plexity pattern. Generally, in ketamine/xylazineanesthesia the brain complexity was higher at cor-tical and hippocampal level with respect to nem-butal anesthesia (Figs. 5(a) left brain, 5(b) rightbrain). During stable ketamine/xylazine anesthesiathere was a dominance of hippocampal complexity(Fig. 2(a) left brain, 2(b) right brain), while in nem-butal anesthesia the complexity dominance movedto the pontine level, and ponto-hippocampo-corticalcomplexity gradient was established (Fig. 4(a) leftbrain, 2(b) right brain).

A major problem in anesthesia is to obtainobjective measures of anesthetic depth.20 Mayer-Kress and Layne21 were the first who questionedwhether nonlinear EEG analysis (using the corre-lation dimension) can be used to estimate anes-thetic depth. Their results would seem to argueagainst the use of this nonlinear quantifier as thesimple measure of anesthetic depth. However, Wattand Hameroff22 concluded that correlation dimen-sion may be of interest for characterization thethree states of consciousness — namely, the corre-lation dimension decreases as the anesthetic depth

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increases. More recently, correlation dimension,1,23

Shannon entropy24 and approximate entropy2 areconfirmed as useful. Some studies,4,6,7 have shownthat the Higuchi’s fractal dimension method maybe successfully applied for analysis of EEG signalsfor monitoring the depth of anesthesia or sedationin fentanyl and propofol anesthesia.

Our study evidenced that this is also an impor-tant issue for analysis and explanations of theresults that we get using any “in vivo” animal anes-thetized model. Using Higuchi fractal dimension wewere able to detect distinct inter-structure complex-ity (distinct degree of neuronal activity), and touncover that the poligraphically and behaviorallydefined stable anesthetized state as equal duringexperiment in both anesthesias, is not the samewith respect to degree of neuronal activity (degreeof inhibition) at different brain levels.

Ketamine/xylazine mixture and nembutal anes-thesia are commonly used anesthetics for labora-tory rats as well as in humans. Xylazine is anα2 adrenoreceptors agonist that is a very com-monly used sedative and muscle relaxant. It iswell known that the ketamine and nembutal havethe opposite main mechanisms of action. Whileketamine antagonizes glutamatergic receptors (glu-tamate is one of the brain excitatory neuro-transmitter) and suppresses excitation within thebrain, nembutal is the agonist of GABA-ergicreceptors (GABA is the main brain inhibitoryneurotransmitter) through which it potentiatesinhibition within the brain. Beside the main mech-anisms of action, the opposite effects of ketamineversus nembutal has been evidenced also on acetil-choline release in Sprague-Dawley rats. In con-trast to ketamine which increases acetil-cholinerelease in hippocampus and frontal cortex, buthad no effect in the striatum, nembutal decreasesacetil-choline release in all structures.25,26 Animportant role of ketamine in hippocampal acetil-choline release is explained through the evidenceof the highest density of NMDA receptors in rathippocampus.27 Ketamine is a unique anestheticthat interacts with NMDA, opioid, monoamin-ergic and muscarinergic receptors and voltage-sensitive Ca++ channels, but does not interactwith GABA receptors.28 The non-NMDA mech-anism of ketamine action is through an increaseof noradrenaline and acetyl-choline, while nembu-tal does not have such effect.25,28,29,30 The char-acteristic post-anesthetic increase in noradrenalinerelease caused by ketamine also might contribute

to psychomimetic effects of ketamine such as vividdreams, agitation and illusions.29 Generally, NMDAreceptor antagonism or GABAA receptor activationhas been considered as important target sites in thegeneral anesthesia induction.31 From the structurerelated point of view the cerebral cortex has beenrecognized as a target site of general anesthesia,32

while the medial prefrontal cortex as the mostimportant brain site for manifestation of emotionalbehavior.33

Our results have proved in both anesthesias,in terms of cortical EEG complexity, the cortexas a main target of loss of consciousness in gen-eral anesthesia. Although in ketamine vs. nem-butal anesthesia there was a general higher EEGcomplexity at cortical and hippocampal level, inketamine anesthesia the dominant EEG complexitystill stays at the limbic cortical level (hippocam-pus), while in nembutal anesthesia the EEG com-plexity decrement order turns to start from thepons through hippocampus to cortex. Although werecorded pontine EEG only on the right brain side,we assume that the same EEG complexity pat-tern gradient is established on the left brain sideunder nembutal anesthesia. On the bases of normal-ized amplitude spectra shown on Fig. 3 the differ-ences in the inter-structure complexity in ketaminevs. nembutal anesthesia were particularly recog-nized as more prominent presence of the ampli-tudes within 5–10 Hz frequency range in ketamineinduced anesthesia. Our results also indicate thatgeneralized potentiation of the GABA effects bynembutal within the brain induced suppressionof this particular frequency range activity at allbrain levels. So far we can only speculate thatthis generalized higher activity within this fre-quency range under ketamine anesthesia yieldsgenerally higher brain complexity with respect tonembutal anesthesia (Figs. 5(a) and 5(b)). Pre-cise quantitative contributions of particular fre-quency bands to the detected level of FD could beassessed if a dedicated series of simulations wereperformed (possibly based on combining signalsobtained by different band-pass filtering of the orig-inal signal).

We have shown that global neuronal inhibi-tion caused by different mechanisms of anestheticaction (suppression of excitation through the blockof glutamate NMDA receptors by ketamine orpotentiation of inhibition through the activationof GABA receptors by nembutal) induces distinctinter-structure EEG complexity pattern. This study

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122 S. Spasic et al.

has proved the Higuchi fractal dimension as a valu-able tool for measuring the anesthesia inducedinter-structure EEG complexity.

ACKNOWLEDGMENTS

The authors thank Prof. Dimitris Kugiumtzis forMATLAB software package for surrogate data gen-eration. This work was supported by Serbian Min-istry of Science and Technological Development[Project No. 173022] and by NIH [grant AG16303].

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