Toward a theory of the general-anesthetic-induced phase transition of the cerebral cortex. II. Numerical simulations, spectral entropy, and correlation times

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: $H\propto -\left(\ln T\right).\mathrm{}$ 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.