Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLoS Comput Biol. 2021 Aug 18;17(8):e1009280. doi: 10.1371/journal.pcbi.1009280. eCollection 2021 Aug.
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.
氯胺酮是一种 NMDA 受体拮抗剂，常用于维持全身麻醉。在麻醉剂量下，氯胺酮引起高功率伽马（25-50 Hz）振荡，与慢德尔塔（0.1-4 Hz）振荡交替。这些动力学在非人类灵长类动物（NHP）的局部场电位（LFP）和人类受试者的脑电图（EEG）记录中很容易观察到。然而，这些动力学的详细统计分析尚未报道。我们使用隐马尔可夫模型（HMM）来描述氯胺酮的神经动力学。HMM 的观测值是七个典型频率带（0 到 50 Hz）内的频谱功率序列，其中功率在每个频带内平均，并在 0 到 1 之间缩放。我们将观测值建模为多元贝塔概率分布的实现，该分布取决于离散值潜在状态过程，其状态转换遵循马尔可夫动力学。使用期望最大化算法，我们将此贝塔-HMM 拟合到 2 只 NHP 的 LFP 记录中，并分别拟合到 9 名接受氯胺酮麻醉剂量的人类受试者的 EEG 记录中。我们的贝塔-HMM 框架为实验数据分析提供了有用的工具。估计的贝塔-HMM 参数和最优状态轨迹共同揭示了一种交替状态模式，主要由伽马和慢德尔塔活动组成。两只 NHP 的伽马活动的平均持续时间为 2.2s([1.7,2.8]s)和 1.2s([0.9,1.5]s)，人类受试者的平均持续时间为 2.5s([1.7,3.6]s)。两只 NHP 的慢德尔塔活动的平均持续时间为 1.6s([1.2,2.0]s)和 1.0s([0.8,1.2]s)，人类受试者的平均持续时间为 1.8s([1.3,2.4]s)。我们对交替的伽马慢德尔塔活动的描述揭示了五个子状态，它们显示出有规律的顺序转换。这些定量见解可以为产生节律的神经元电路模型的发展提供信息，这些模型为这一现象以及氯胺酮如何产生不同的觉醒状态提供了机械性的见解。
