PubMed · 5813078
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R C Gunther, P J Schaner, B J McIntosh, G A Gronert. Halothane for burn anesthesia.. https://pubmed.ncbi.nlm.nih.gov/5813078/
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Many properties of nitric oxide, NO, (localization, diffusiveness, half-life, vasodilatory affects) have supported its potential role in mediating the link between local cerebral activity and blood flow. However, evidence that both supports and refutes a role for NO in functional hyperemia have been presented. The present study employed multiple nitric oxide synthase inhibitors, two anesthetic regimes and laser-Doppler flowmetry to test the hypothesis that NO is critically involved in mediating the functional hyperemic response within rodent whisker-barrel cortex (WBC). In urethane anesthetized animals, functional hyperemic responses were obtained both before and after 1 mg/kg atropine infusion, 30 mg/kg i.v. L-NAME (N-Nitro-L-arginine methylester) infusion, 30 mg/kg L-NA (N-Nitro-L-arginine) infusion or 25 mg/kg 7-NI (7-nitroindazole). L-NAME was also tested in a group of animals pretreated with halothane before urethane anesthesia. Neither the magnitude of the blood flow response nor its time course was altered by NO blockade or atropine administration when compared to pre-infusion controls in urethane anesthetized rats. In contrast, animals that were pretreated with halothane exhibited a 33% inhibition of functional hyperemia after L-NAME administration. Taken together, these data do not support a primary role for NO in rat WBC functional hyperemia and suggest that previous reports of inhibition may have been secondary to the anesthesia employed.
In a recent paper the authors developed a stochastic model for the response of the cerebral cortex to a general anesthetic agent. The model predicted that there would be an anesthetic-induced phase change at the point of transition into unconsciousness, manifested as a divergence in the electroencephalogram spectral power, and a change in spectral energy distribution from being relatively broadband in the conscious state to being strongly biased towards much lower frequencies in the unconscious state. Both predictions have been verified in recent clinical measurements. In the present paper we extend the model by calculating the equilibrium distribution function for the cortex, allowing us to establish a correspondence between the cortical phase transition and the more familiar thermodynamic phase transitions. This correspondence is achieved by first identifying a cortical free energy function, then by postulating that there exists an inverse relationship between an anesthetic effect and a quantity we define as cortical excitability, which plays a role analogous to temperature in thermodynamic phase transitions. We follow standard thermodynamic theory to compute a cortical entropy and a cortical "heat capacity," and we investigate how these will vary with anesthetic concentration. The significant result is the prediction that the entropy will decrease discontinuously at the moment of induction into unconsciousness, concomitant with a release of "latent heat" which should manifest as a divergence in the analogous heat capacity. There is clear clinical evidence of heat capacity divergence in historical anesthetic-effect measurements performed in 1977 by Stullken et al. [Anesthesiology 46, 28 (1977)]. The discontinuous step change in cortical entropy suggests that the cortical phase transition is analogous to a first-order thermodynamic transition in which the comatose-quiescent state is strongly ordered, while the active cortical state is relatively disordered.
In our two recent papers [M.L. Steyn-Ross et al., Phys. Rev. E 60, 7299 (1999); 64, 011917 (2001)] we presented clinical evidence for a general anesthetic-induced phase change in the cerebral cortex, and showed how the significant features of the cortical phase change (biphasic power surge, spectral energy redistribution, "heat capacity" divergence), could be explained using a stochastic single-macrocolumn model of the cortex. The model predictions were based on rather strong "adiabatic" assumptions which assert that the mean-field excitatory and inhibitory macrocolumn voltages are "slow" variables whose equilibration times are much longer than those of the input "currents" that drive the macrocolumn. In the present paper we test the adiabatic assumption by running numerical simulations of the stochastic differential equations. These simulations confirm the number and nature of the steady-state solutions, the growth of fluctuation power at transition, and the redistribution of spectral energy towards lower frequencies. We use spectral entropy to quantify these changes in the power spectral density, and to show that the spectral entropy should decrease markedly at the point of transition. This prediction agrees with recent clinical findings by Viertiö-Oja and colleagues [J. Clinical Monitoring Computing 16, 60 (2000)]. Our modeling work shows that there is an inverse relationship between spectral entropy H and correlation time T of the soma-voltage fluctuations: H inversely proportional to (ln T). In a theoretical analysis we prove that this proportionality becomes exact for an ideal Lorentzian process. These findings suggest that by monitoring the changes in EEG correlation time, it should be possible to track changes in the state of patient consciousness.