| Popis: |
Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes bursts of 30-50 Hz oscillations alternating with 0.1 to 10 Hz oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in 10 Hz frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma burst and slow oscillation activity, as well as intermediate states in between. The mean duration of the gamma burst state was 2.5s([1.9,3.4]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.7s([1.9,3.8]s) for the human subjects. The mean duration of the slow oscillation state was 1.6s([1.1,2.5]s) and 0.7s([0.6,0.9]s) for the two NHPs, and 2.8s([1.9,4.3]s) for the human subjects. Our beta-HMM framework provides a useful tool for experimental data analysis. Our characterizations of the gamma-burst process offer detailed, quantitative constraints that can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal. |
