Effective Brain State Estimation During Propofol-Induced Sedation Using Advanced EEG Microstate Spectral Analysis

Brain states are patterns of neuronal synchrony, and the electroencephalogram (EEG) microstate provided a promising tool to non-invasively characterize and analyze the synchronous neural firing. However, the topographical spectral information for each predominate microstate is still unclear during the switch of consciousness, such as sedation, and the practical usage of the EEG microstate is worth probing. Also, the mechanism behind the anesthetic-induced alternations of brain states remains poorly understood. In this study, a novel EEG microstate spectral analysis was utilized using multivariate empirical mode decomposition in Hilbert-Huang transform. The practicability was further investigated in scalp EEG recordings during the propofol-induced transition of consciousness. The process of transition from awake to moderate sedation was accompanied by apparent increases in microstate (A, B, and F) energy, especially in the whole-brain delta band, frontal alpha band and beta band. In comparison to other effective EEG-based parameters that commonly used to measure anesthetic depth, utilizing the selected spectral features reached better performance (80% sensitivity, 90% accuracy) to estimate the brain states during sedation. The changes in microstate energy also exhibited high correlations with individual behavioral data during sedation. In a nutshell, the EEG microstate spectral analysis is an effective method to estimate brain states during propofol-induced sedation, giving great insights into the underlying mechanism. The generated spectral features can be promising markers to dynamically assess the consciousness level.

[1]  Lei Xie,et al.  Fast Multivariate Empirical Mode Decomposition , 2018, IEEE Access.

[2]  A. Hudetz,et al.  Differential Effects of Deep Sedation with Propofol on the Specific and Nonspecific Thalamocortical Systems: A Functional Magnetic Resonance Imaging Study , 2013, Anesthesiology.

[3]  G. Tononi,et al.  *Both authors contributed equally to this manuscript. , 2022 .

[4]  E. Brown,et al.  General anesthesia, sleep, and coma. , 2010, The New England journal of medicine.

[5]  Christoph M. Michel,et al.  EEG microstates of wakefulness and NREM sleep , 2012, NeuroImage.

[6]  Denis Brunet,et al.  Topographic ERP Analyses: A Step-by-Step Tutorial Review , 2008, Brain Topography.

[7]  Gabriel Rilling,et al.  Bivariate Empirical Mode Decomposition , 2007, IEEE Signal Processing Letters.

[8]  Enzo Tagliazucchi,et al.  Narcoleptic Patients Show Fragmented EEG-Microstructure During Early NREM Sleep , 2014, Brain Topography.

[9]  Dietrich Lehmann,et al.  The functional significance of EEG microstates—Associations with modalities of thinking , 2016, NeuroImage.

[10]  Norden E. Huang,et al.  A review on Hilbert‐Huang transform: Method and its applications to geophysical studies , 2008 .

[11]  R. Zatorre,et al.  Cortical Processing of Complex Auditory Stimuli during Alterations of Consciousness with the General Anesthetic Propofol , 2006, Anesthesiology.

[12]  M. Sigman,et al.  Signature of consciousness in the dynamics of resting-state brain activity , 2015, Proceedings of the National Academy of Sciences.

[13]  I. Rampil,et al.  Changes in EEG spectral edge frequency correlate with the hemodynamic response to laryngoscopy and intubation. , 1987, Anesthesiology.

[14]  D. Lehmann,et al.  Adaptive segmentation of spontaneous EEG map series into spatially defined microstates. , 1993, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[15]  J. Bruhn,et al.  Approximate Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect during Desflurane Anesthesia , 2000, Anesthesiology.

[16]  Dimitri Van De Ville,et al.  Electroencephalographic Resting-State Networks: Source Localization of Microstates , 2017, Brain Connect..

[17]  I. Rampil A Primer for EEG Signal Processing in Anesthesia , 1998, Anesthesiology.

[18]  Satoshi Hagihira,et al.  The Relationship Between Bispectral Index and Electroencephalographic Parameters During Isoflurane Anesthesia , 2004, Anesthesia and analgesia.

[19]  Gang Wang,et al.  Monitoring the Depth of Anesthesia Through the Use of Cerebral Hemodynamic Measurements Based on Sample Entropy Algorithm , 2020, IEEE Transactions on Biomedical Engineering.

[20]  Karl J. Friston,et al.  Behavioral / Systems / Cognitive Connectivity Changes Underlying Spectral EEG Changes during Propofol-Induced Loss of Consciousness , 2012 .

[21]  Danilo P. Mandic,et al.  Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis , 2013, IEEE Signal Processing Magazine.

[22]  Dietrich Lehmann,et al.  Classes of Multichannel EEG Microstates in Light and Deep Hypnotic Conditions , 2007, Brain Topography.

[23]  Emery N. Brown,et al.  Tracking brain states under general anesthesia by using global coherence analysis , 2011, Proceedings of the National Academy of Sciences.

[24]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[25]  Nai Ding,et al.  Assessing the depth of language processing in patients with disorders of consciousness , 2020, Nature Neuroscience.

[26]  M. Takashina,et al.  Practical Issues in Bispectral Analysis of Electroencephalographic Signals , 2001, Anesthesia and analgesia.

[27]  Ram Adapa,et al.  Brain Connectivity Dissociates Responsiveness from Drug Exposure during Propofol-Induced Transitions of Consciousness , 2016, PLoS Comput. Biol..

[28]  Benjamin A. Seitzman,et al.  Cognitive manipulation of brain electric microstates , 2017, NeuroImage.

[29]  Enzo Tagliazucchi,et al.  Dynamic functional connectivity and brain metastability during altered states of consciousness , 2017, NeuroImage.

[30]  Richard Rogers,et al.  Cortical and Subcortical Connectivity Changes during Decreasing Levels of Consciousness in Humans: A Functional Magnetic Resonance Imaging Study using Propofol , 2010, The Journal of Neuroscience.

[31]  D. P. Mandic,et al.  Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[32]  Charles M. Gaona,et al.  Stable and dynamic cortical electrophysiology of induction and emergence with propofol anesthesia , 2010, Proceedings of the National Academy of Sciences.

[33]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[34]  Dietrich Lehmann,et al.  Millisecond by Millisecond, Year by Year: Normative EEG Microstates and Developmental Stages , 2002, NeuroImage.

[35]  E. Brown,et al.  Thalamocortical Mechanisms for the Anteriorization of Alpha Rhythms during Propofol-Induced Unconsciousness , 2013, The Journal of Neuroscience.

[36]  D. Lehmann,et al.  Reference-free identification of components of checkerboard-evoked multichannel potential fields. , 1980, Electroencephalography and clinical neurophysiology.

[37]  Yamin Li,et al.  Non-Canonical Microstate Becomes Salient in High Density EEG During Propofol-Induced Altered States of Consciousness , 2020, Int. J. Neural Syst..

[38]  Dimitri Van De Ville,et al.  BOLD correlates of EEG topography reveal rapid resting-state network dynamics , 2010, NeuroImage.

[39]  Srivas Chennu,et al.  Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness , 2017, Brain Topography.

[40]  Danilo P. Mandic,et al.  Application of multivariate empirical mode decomposition for seizure detection in EEG signals , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[41]  Dietrich Lehmann,et al.  Spatial analysis of evoked potentials in man—a review , 1984, Progress in Neurobiology.

[42]  E. Brown,et al.  Clinical Electroencephalography for Anesthesiologists: Part I Background and Basic Signatures , 2015, Anesthesiology.

[43]  D. Lehmann,et al.  Segmentation of brain electrical activity into microstates: model estimation and validation , 1995, IEEE Transactions on Biomedical Engineering.

[44]  Laura D. Lewis,et al.  Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness , 2012, Proceedings of the National Academy of Sciences.

[45]  R. D. Pascual-Marqui,et al.  The EEG microstate topography is predominantly determined by intracortical sources in the alpha band , 2017, NeuroImage.

[46]  Erik W. Jensen,et al.  EEG complexity as a measure of depth of anesthesia for patients , 2001, IEEE Trans. Biomed. Eng..

[47]  William Wisden,et al.  Altered Activity in the Central Medial Thalamus Precedes Changes in the Neocortex during Transitions into Both Sleep and Propofol Anesthesia , 2014, The Journal of Neuroscience.

[48]  J. Sigl,et al.  Anesthesia awareness and the bispectral index. , 2008, The New England journal of medicine.

[49]  Thomas Koenig,et al.  EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review , 2017, NeuroImage.

[50]  Emery N. Brown,et al.  Electroencephalogram signatures of loss and recovery of consciousness from propofol , 2013, Proceedings of the National Academy of Sciences.

[51]  Juliane Britz,et al.  EEG microstate sequences in healthy humans at rest reveal scale-free dynamics , 2010, Proceedings of the National Academy of Sciences.