A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms
| Autor: | Shubham Chamadia, Indie C. Garwood, Oluwaseun Akeju, Sourish Chakravarty, Pegah Kahali, Emery N. Brown, Earl K. Miller, Jacob A. Donoghue, Meredith Mahnke |
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| Rok vydání: | 2021 |
| Předmět: |
Physiology
General Anesthesia Markov models Local field potential Electroencephalography 0302 clinical medicine Anesthesiology Medicine and Health Sciences Gamma Rhythm Anesthesia Hidden Markov models Statistical physics Biology (General) Hidden Markov model Clinical Neurophysiology Physics Brain Mapping 0303 health sciences Ecology medicine.diagnostic_test Pharmaceutics Applied Mathematics Simulation and Modeling Brain Drugs Markov Chains Physical sciences Electrophysiology Bioassays and Physiological Analysis Brain Electrophysiology Computational Theory and Mathematics Modeling and Simulation Probability distribution Ketamine Algorithms Research Article medicine.drug QH301-705.5 Imaging Techniques Neurophysiology Neuroimaging Research and Analysis Methods 03 medical and health sciences Cellular and Molecular Neuroscience Drug Therapy Genetics medicine Animals Humans Pain Management Molecular Biology Ecology Evolution Behavior and Systematics Probability Anesthetics 030304 developmental biology Pharmacology Markov chain Electrophysiological Techniques Biology and Life Sciences Probability theory Random Variables Probability Distribution Anesthetic Macaca Clinical Medicine Excitatory Amino Acid Antagonists Random variable Mathematics 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | PLoS Computational Biology PLoS Computational Biology, Vol 17, Iss 8, p e1009280 (2021) |
| ISSN: | 1553-7358 |
| DOI: | 10.1371/journal.pcbi.1009280 |
| Popis: | Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes high power gamma (25-50 Hz) oscillations alternating with slow-delta (0.1-4 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 seven canonical 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. Our beta-HMM framework provides a useful tool for experimental data analysis. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma and slow-delta activities. The mean duration of the gamma activity was 2.2s([1.7,2.8]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.5s([1.7,3.6]s) for the human subjects. The mean duration of the slow-delta activity was 1.6s([1.2,2.0]s) and 1.0s([0.8,1.2]s) for the two NHPs, and 1.8s([1.3,2.4]s) for the human subjects. Our characterizations of the alternating gamma slow-delta activities revealed five sub-states that show regular sequential transitions. These quantitative insights 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. 1 Author summary Monitoring brain activity during anesthesia can provide insights into the underlying mechanisms of how anesthetics elicit altered states of consciousness. Ketamine, a commonly used anesthetic, is known to cause short duration bursts of high frequency electrophysiological activity in the brain, but the neural mechanisms underlying this activity are not well understood. A key limitation in developing accurate models of the underlying mechanism is a lack of detailed knowledge of the dynamic structure and spectral properties of ketamine-induced oscillations. In this work, we address this limitation by developing a statistical framework to quantify ketamine-induced neural activity. Our framework is based on a hidden Markov model, which assumes that the neural activity switches among discrete states, each of which has its own distinctive probabilistic spectral representation. By estimating this versatile statistical model from electrophysiology data, we generated detailed descriptions of the dynamic properties and oscillatory signatures associated with ketamine-induced neurophysiological states in non-human primates and in human patients. Furthermore, we identified additional ketamine-induced states that have not yet been reported. In summary, our detailed quantitative descriptions of ketamine-induced spectra can aid further developments of neurophysiological mechanistic models of ketamine as well as biomarker discovery for clinical anesthesia monitoring. |
| Databáze: | OpenAIRE |
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