State-Space Global Coherence to Estimate the Spatio-Temporal Dynamics of the Coordinated Brain Activity

Yousefi, Ali; Fard, Reza Saadati; Eden, Uri T.; Brown, Emery N.

doi:10.1109/embc.2019.8856634

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…Our work advances the development and application of LFP or EEG data analysis tools. The beta-HMM analysis framework falls under the broad category of research aimed at developing time-frequency state space models to analyze neural dynamics [43,52,[62][63][64][65], and applying them in principled interpretation of neural time-series data for unconsciousness research [11,62]. A few key distinguishing features of the beta-HMM analysis framework are as follows.…”

Section: Discussionmentioning

confidence: 99%

A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms

et al. 2021

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“…Our work advances the development and application of LFP or EEG data analysis tools. The beta-HMM analysis framework falls under the broad category of research aimed at developing time-frequency state space models to analyze neural dynamics [41,50,64,65,66,67], and applying them in principled interpretation of neural time-series data for unconsciousness research [12,64]. A few key distinguishing features of the 23…”

Section: Discussionmentioning

confidence: 99%

A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque LFP and human EEG

Garwood

Chakravarty

Donoghue

et al. 2020

Preprint

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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.

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“…is defined as a function of covariates [15]. Note that under the mild wide-stationary assumption of time-series, the Fourier transform at different frequencies per each time interval become statistically independent; as a result, we build one model per each frequency.…”

Section: Covariate-dependent Coherence Analysismentioning

confidence: 99%

“…The proposed model for the cross-spectral matrix assumes that eigenvectors are stationary over time and eigenvalues change as a function of the state process, assumptions reflecting new findings, where engagement and disengagement of different networks over time shape complex brain function [15]. Other forms of cross-spectral matrix decomposition like Bollerslev decomposition are used for time-varying covariance matrices [19], in which the correlation matrix is defined as a function of the state processes.…”

Section: State-space Coherence Analysismentioning

confidence: 99%

“…For example, Multivariate Stochastic Volatility (MSV) model with a dynamic covariance model is used in optimal portfolio and risk management strategies [23,24]. Similar solutions have been used to study associations across neural data in the time domain [15]. Despite the similarity between these models and SSCoh, SSCoh has a solid modeling foundation, provides a mechanistics understanding of network level neural synchrony, and the statistical properties of the observation are Gaussian and tractable.…”

Section: State-space Coherence Analysismentioning

confidence: 99%

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Analysis of Distributed Neural Synchrony through State-Space Coherence Analysis

Fard

Arai

Eden

et al. 2020

Preprint

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Established methods to track the dynamics of neural representations focus at the level of individual neurons for spiking data, and individual or pair of channels for local field potentials. However, our understanding of neural function and computation has moved toward an integrative view, based upon coordinated activity of multiple neural populations across brain areas. To draw network-level inferences of brain function, we propose a new modeling framework that combines the state-space model and cross-spectral matrix estimates – this is called state-space coherence (SSCoh). We define elements of the SSCoh and derive system identification and approximate filter solution for multivariate space processes. We expand SCoh for mixed observation processes, where the observation includes different modalities of neural data including local filed potential and spiking activity. Finally, we show an application of the framework to study neural synchrony across different brain nodes of a task participant performing Stroop task under different distraction levels.

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State-Space Global Coherence to Estimate the Spatio-Temporal Dynamics of the Coordinated Brain Activity

Cited by 7 publications

References 15 publications

A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms

A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms

A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque LFP and human EEG

Analysis of Distributed Neural Synchrony through State-Space Coherence Analysis

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