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Hermann G, Tödt I, Tagliazucchi E, Todtenhaupt IK, Laufs H, von Wegner F. Propofol Reversibly Attenuates Short-Range Microstate Ordering and 20 Hz Microstate Oscillations. Brain Topogr 2024; 37:329-342. [PMID: 38228923 DOI: 10.1007/s10548-023-01023-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/18/2023] [Indexed: 01/18/2024]
Abstract
Microstate sequences summarize the changing voltage patterns measured by electroencephalography, using a clustering approach to reduce the high dimensionality of the underlying data. A common approach is to restrict the pattern matching step to local maxima of the global field power (GFP) and to interpolate the microstate fit in between. In this study, we investigate how the anesthetic propofol affects microstate sequence periodicity and predictability, and how these metrics are changed by interpolation. We performed two frequency analyses on microstate sequences, one based on time-lagged mutual information, the other based on Fourier transform methodology, and quantified the effects of interpolation. Resting-state microstate sequences had a 20 Hz frequency peak related to dominant 10 Hz (alpha) rhythms, and the Fourier approach demonstrated that all five microstate classes followed this frequency. The 20 Hz periodicity was reversibly attenuated under moderate propofol sedation, as shown by mutual information and Fourier analysis. Characteristic microstate frequencies could only be observed in non-interpolated microstate sequences and were masked by smoothing effects of interpolation. Information-theoretic analysis revealed faster microstate dynamics and larger entropy rates under propofol, whereas Shannon entropy did not change significantly. In moderate sedation, active information storage decreased for non-interpolated sequences. Signatures of non-equilibrium dynamics were observed in non-interpolated sequences, but no changes were observed between sedation levels. All changes occurred while subjects were able to perform an auditory perception task. In summary, we show that low dose propofol reversibly increases the randomness of microstate sequences and attenuates microstate oscillations without correlation to cognitive task performance. Microstate dynamics between GFP peaks reflect physiological processes that are not accessible in interpolated sequences.
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Affiliation(s)
- Gesine Hermann
- Department of Neurology, Christian-Albrechts University, University Hospital Schleswig Holstein, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
| | - Inken Tödt
- Institute of Sexual Medicine & Forensic Psychiatry and Psychotherapy, Christian-Albrechts University, Schwanenweg 24, 24105, Kiel, Germany
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
| | - Inga Karin Todtenhaupt
- Department of Neurology, Christian-Albrechts University, University Hospital Schleswig Holstein, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
| | - Helmut Laufs
- Department of Neurology, Christian-Albrechts University, University Hospital Schleswig Holstein, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
| | - Frederic von Wegner
- School of Biomedical Sciences, UNSW, Wallace Wurth Building, Kensington, NSW, 2052, Australia.
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2
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Zanesco AP. Normative Temporal Dynamics of Resting EEG Microstates. Brain Topogr 2024; 37:243-264. [PMID: 37702825 DOI: 10.1007/s10548-023-01004-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/23/2023] [Indexed: 09/14/2023]
Abstract
The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.
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Affiliation(s)
- Anthony P Zanesco
- Department of Psychology, University of Miami, Coral Gables, FL, USA.
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3
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Wiemers MC, Laufs H, von Wegner F. Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep. Brain Topogr 2024; 37:312-328. [PMID: 37253955 DOI: 10.1007/s10548-023-00971-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 05/11/2023] [Indexed: 06/01/2023]
Abstract
The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.
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Affiliation(s)
- Milena C Wiemers
- Department of Neurology and Clinical Neurophysiology, Lüneburg Hospital, Bögelstrasse 1, 21339, Lüneburg, Germany
| | - Helmut Laufs
- Department of Neurology, Christian-Albrechts University Kiel, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
| | - Frederic von Wegner
- School of Biomedical Sciences, University of New South Wales, Wallace Wurth Building, Kensington, NSW, 2052, Australia.
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4
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Černý F, Piorecká V, Kliková M, Kopřivová J, Bušková J, Piorecký M. All-night spectral and microstate EEG analysis in patients with recurrent isolated sleep paralysis. Front Neurosci 2024; 18:1321001. [PMID: 38389790 PMCID: PMC10882627 DOI: 10.3389/fnins.2024.1321001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 01/25/2024] [Indexed: 02/24/2024] Open
Abstract
The pathophysiology of recurrent isolated sleep paralysis (RISP) has yet to be fully clarified. Very little research has been performed on electroencephalographic (EEG) signatures outside RISP episodes. This study aimed to investigate whether sleep is disturbed even without the occurrence of a RISP episode and in a stage different than conventional REM sleep. 17 RISP patients and 17 control subjects underwent two consecutive full-night video-polysomnography recordings. Spectral analysis was performed on all sleep stages in the delta, theta, and alpha band. EEG microstate (MS) analysis was performed on the NREM 3 phase due to the overall high correlation of subject template maps with canonical templates. Spectral analysis showed a significantly higher power of theta band activity in REM and NREM 2 sleep stages in RISP patients. The observed rise was also apparent in other sleep stages. Conversely, alpha power showed a downward trend in RISP patients' deep sleep. MS maps similar to canonical topographies were obtained indicating the preservation of prototypical EEG generators in RISP patients. RISP patients showed significant differences in the temporal dynamics of MS, expressed by different transitions between MS C and D and between MS A and B. Both spectral analysis and MS characteristics showed abnormalities in the sleep of non-episodic RISP subjects. Our findings suggest that in order to understand the neurobiological background of RISP, there is a need to extend the analyzes beyond REM-related processes and highlight the value of EEG microstate dynamics as promising functional biomarkers of RISP.
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Affiliation(s)
- Filip Černý
- Faculty of Biomedical Engineering, Czech Technical University, Prague, Czechia
- Sleep and Chronobiology Research Center, National Institute of Mental Health, Klecany, Czechia
| | - Václava Piorecká
- Faculty of Biomedical Engineering, Czech Technical University, Prague, Czechia
- Sleep and Chronobiology Research Center, National Institute of Mental Health, Klecany, Czechia
| | - Monika Kliková
- Sleep and Chronobiology Research Center, National Institute of Mental Health, Klecany, Czechia
| | - Jana Kopřivová
- Sleep and Chronobiology Research Center, National Institute of Mental Health, Klecany, Czechia
- Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Jitka Bušková
- Sleep and Chronobiology Research Center, National Institute of Mental Health, Klecany, Czechia
- Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Marek Piorecký
- Faculty of Biomedical Engineering, Czech Technical University, Prague, Czechia
- Sleep and Chronobiology Research Center, National Institute of Mental Health, Klecany, Czechia
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5
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Zhu M, Gong Q. EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training. Front Neurosci 2023; 17:1254423. [PMID: 38148944 PMCID: PMC10750374 DOI: 10.3389/fnins.2023.1254423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 11/21/2023] [Indexed: 12/28/2023] Open
Abstract
Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the few interventions that can temporarily inhibit tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. In this study, RI effect of TMNMT in tinnitus was investigated mainly using behavioral tests, EEG spectral and microstate analysis. To our knowledge, this study is the first to investigate RI effect of TMNMT. A total of 44 participants with tinnitus were divided into TMNMT group (22 participants; ECnm, NMnm, RInm represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by TMNMT music, respectively) and Placebo control group (22 participants; ECpb, PBpb, RIpb represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by Placebo music, respectively) in a single-blind manner. Behavioral tests, EEG spectral analysis (covering delta, theta, alpha, beta, gamma frequency bands) and microstate analysis (involving four microstate classes, A to D) were employed to evaluate RI effect of TMNMT. The results of the study showed that TMNMT had a stronger inhibition ability and longer inhibition time according to the behavioral tests compared to Placebo. Spectral analysis showed that RI effect of TMNMT increased significantly the power spectral density (PSD) of delta, theta bands and decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of TMNMT had shorter duration (microstate B, microstate C), higher Occurrence (microstate A, microstate C, microstate D), Coverage (microstate A) and transition probabilities (microstate A to microstate B, microstate A to microstate D and microstate D to microstate A). Meanwhile, RI effect of Placebo decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of Placebo had shorter duration (microstate C, microstate D), higher occurrence (microstate B, microstate C), lower coverage (microstate C, microstate D), higher transition probabilities (microstate A to microstate B, microstate B to microstate A). It was also found that the intensity of tinnitus symptoms was significant positively correlated with the duration of microstate B in five subgroups (ECnm, NMnm, RInm, ECpb, PBpb). Our study provided valuable experimental evidence and practical applications for the effectiveness of TMNMT as a novel music therapy for tinnitus. The observed stronger residual inhibition (RI) ability of TMNMT supported its potential applications in tinnitus treatment. Furthermore, the temporal dynamics of EEG microstates serve as novel functional and trait markers of synchronous brain activity that contribute to a deep understanding of the neural mechanism underlying TMNMT treatment for tinnitus.
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Affiliation(s)
- Min Zhu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Qin Gong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
- School of Medicine, Shanghai University, Shanghai, China
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6
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EEG microstate in obstructive sleep apnea patients. Sci Rep 2021; 11:17178. [PMID: 34433839 PMCID: PMC8387348 DOI: 10.1038/s41598-021-95749-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 07/28/2021] [Indexed: 02/07/2023] Open
Abstract
Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.
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7
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Li Y, Shi W, Liu Z, Li J, Wang Q, Yan X, Cao Z, Wang G. Effective Brain State Estimation During Propofol-Induced Sedation Using Advanced EEG Microstate Spectral Analysis. IEEE J Biomed Health Inform 2021; 25:978-987. [PMID: 32749987 DOI: 10.1109/jbhi.2020.3008052] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Brain states are patterns of neuronal synchrony, and the electroencephalogram (EEG) microstate provides a promising tool to 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, an advanced 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 the awake baseline 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, using 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.
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8
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Wang H, Wang Y, Zhang Y, Dong Z, Yan F, Song D, Wang Q, Huang L. Differentiating propofol-induced altered states of consciousness using features of EEG microstates. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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9
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Sun L, Hu L, Ren G, Yang Y. Musical Tension Associated With Violations of Hierarchical Structure. Front Hum Neurosci 2020; 14:578112. [PMID: 33192408 PMCID: PMC7531224 DOI: 10.3389/fnhum.2020.578112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/27/2020] [Indexed: 11/13/2022] Open
Abstract
Tension is one of the core principles of emotion evoked by music, linking objective musical events and subjective experience. The present study used continuous behavioral rating and electroencephalography (EEG) to investigate the dynamic process of tension generation and its underlying neurocognitive mechanisms; specifically, tension induced by structural violations at different music hierarchical levels. In the experiment, twenty-four musicians were required to rate felt tension continuously in real-time, while listening to music sequences with either well-formed structure, phrase violations, or period violations. The behavioral data showed that structural violations gave rise to increasing and accumulating tension experience as the music unfolded; tension was increased dramatically by structural violations. Correspondingly, structural violations elicited N5 at GFP peaks, and induced decreasing neural oscillations power in the alpha frequency band (8–13 Hz). Furthermore, compared to phrase violations, period violations elicited larger N5 and induced a longer-lasting decrease of power in the alpha band, suggesting a hierarchical manner of musical processing. These results demonstrate the important role of musical structure in the generation of the experience of tension, providing support to the dynamic view of musical emotion and the hierarchical manner of tension processing.
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Affiliation(s)
- Lijun Sun
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Li Hu
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Guiqin Ren
- College of Psychology, Liaoning Normal University, Dalian, China
| | - Yufang Yang
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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10
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von Wegner F, Bauer S, Rosenow F, Triesch J, Laufs H. EEG microstate periodicity explained by rotating phase patterns of resting-state alpha oscillations. Neuroimage 2020; 224:117372. [PMID: 32979526 DOI: 10.1016/j.neuroimage.2020.117372] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 08/08/2020] [Accepted: 09/11/2020] [Indexed: 02/07/2023] Open
Abstract
Spatio-temporal patterns in electroencephalography (EEG) can be described by microstate analysis, a discrete approximation of the continuous electric field patterns produced by the cerebral cortex. Resting-state EEG microstates are largely determined by alpha frequencies (8-12 Hz) and we recently demonstrated that microstates occur periodically with twice the alpha frequency. To understand the origin of microstate periodicity, we analyzed the analytic amplitude and the analytic phase of resting-state alpha oscillations independently. In continuous EEG data we found rotating phase patterns organized around a small number of phase singularities which varied in number and location. The spatial rotation of phase patterns occurred with the underlying alpha frequency. Phase rotors coincided with periodic microstate motifs involving the four canonical microstate maps. The analytic amplitude showed no oscillatory behaviour and was almost static across time intervals of 1-2 alpha cycles, resulting in the global pattern of a standing wave. In n=23 healthy adults, time-lagged mutual information analysis of microstate sequences derived from amplitude and phase signals of awake eyes-closed EEG records showed that only the phase component contributed to the periodicity of microstate sequences. Phase sequences showed mutual information peaks at multiples of 50 ms and the group average had a main peak at 100 ms (10 Hz), whereas amplitude sequences had a slow and monotonous information decay. This result was confirmed by an independent approach combining temporal principal component analysis (tPCA) and autocorrelation analysis. We reproduced our observations in a generic model of EEG oscillations composed of coupled non-linear oscillators (Stuart-Landau model). Phase-amplitude dynamics similar to experimental EEG occurred when the oscillators underwent a supercritical Hopf bifurcation, a common feature of many computational models of the alpha rhythm. These findings explain our previous description of periodic microstate recurrence and its relation to the time scale of alpha oscillations. Moreover, our results corroborate the predictions of computational models and connect experimentally observed EEG patterns to properties of critical oscillator networks.
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Affiliation(s)
- F von Wegner
- School of Medical Sciences, University of New South Wales, Wallace Wurth Building, Kensington, NSW 2052, Australia; Epilepsy Center Frankfurt Rhine-Main, Center of Neurology and Neurosurgery, University Hospital Frankfurt and Center for Personalized Translational Epilepsy Research (CePTER), Goethe University Frankfurt, Frankfurt am Main, Germany.
| | - S Bauer
- Epilepsy Center Frankfurt Rhine-Main, Center of Neurology and Neurosurgery, University Hospital Frankfurt and Center for Personalized Translational Epilepsy Research (CePTER), Goethe University Frankfurt, Frankfurt am Main, Germany
| | - F Rosenow
- Epilepsy Center Frankfurt Rhine-Main, Center of Neurology and Neurosurgery, University Hospital Frankfurt and Center for Personalized Translational Epilepsy Research (CePTER), Goethe University Frankfurt, Frankfurt am Main, Germany
| | - J Triesch
- Frankfurt Institute for Advanced Studies (FIAS), Frankfurt am Main, Germany
| | - H Laufs
- Department of Neurology, Christian-Albrechts University Kiel, Arnold-Heller-Strasse 3, Kiel 24105, Germany
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11
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Krylova M, Alizadeh S, Izyurov I, Teckentrup V, Chang C, van der Meer J, Erb M, Kroemer N, Koenig T, Walter M, Jamalabadi H. Evidence for modulation of EEG microstate sequence by vigilance level. Neuroimage 2020; 224:117393. [PMID: 32971266 DOI: 10.1016/j.neuroimage.2020.117393] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/11/2020] [Accepted: 09/16/2020] [Indexed: 12/25/2022] Open
Abstract
The momentary global functional state of the brain is reflected in its electric field configuration and cluster analytical approaches have consistently shown four configurations, referred to as EEG microstate classes A to D. Changes in microstate parameters are associated with a number of neuropsychiatric disorders, task performance, and mental state establishing their relevance for cognition. However, the common practice to use eye-closed resting state data to assess the temporal dynamics of microstate parameters might induce systematic confounds related to vigilance levels. Here, we studied the dynamics of microstate parameters in two independent data sets and showed that the parameters of microstates are strongly associated with vigilance level assessed both by EEG power analysis and fMRI global signal. We found that the duration and contribution of microstate class C, as well as transition probabilities towards microstate class C were positively associated with vigilance, whereas the sign was reversed for microstate classes A and B. Furthermore, in looking for the origins of the correspondence between microstates and vigilance level, we found Granger-causal effects of vigilance levels on microstate sequence parameters. Collectively, our findings suggest that duration and occurrence of microstates have a different origin and possibly reflect different physiological processes. Finally, our findings indicate the need for taking vigilance levels into consideration in resting-sate EEG investigations.
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Affiliation(s)
- Marina Krylova
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany; Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743 Jena, Germany
| | - Sarah Alizadeh
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany
| | - Igor Izyurov
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743 Jena, Germany; Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany
| | - Vanessa Teckentrup
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA
| | | | - Michael Erb
- Division of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Nils Kroemer
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743 Jena, Germany; Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany; Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany; Max Planck Institute for biological cybernetics, Tübingen, Germany.
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany.
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12
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Shi W, Li Y, Liu Z, Li J, Wang Q, Yan X, Wang G. Non-Canonical Microstate Becomes Salient in High Density EEG During Propofol-Induced Altered States of Consciousness. Int J Neural Syst 2020; 30:2050005. [PMID: 31969080 DOI: 10.1142/s0129065720500057] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Dynamically assessing the level of consciousness is still challenging during anesthesia. With the help of Electroencephalography (EEG), the human brain electric activity can be noninvasively measured at high temporal resolution. Several typical quasi-stable states are introduced to represent the oscillation of the global scalp electric field. These so-called microstates reflect spatiotemporal dynamics of coherent neural activities and capture the switch of brain states within the millisecond range. In this study, the microstates of high-density EEG were extracted and investigated during propofol-induced transition of consciousness. To analyze microstates on the frequency domain, a novel microstate-wise spectral analysis was proposed by the means of multivariate empirical mode decomposition and Hilbert–Huang transform. During the transition of consciousness, a map with a posterior central maximum denoted as microstate F appeared and became salient. The current results indicated that the coverage, occurrence, and power of microstate F significantly increased in moderate sedation. The results also demonstrated that the transition of brain state from rest to sedation was accompanied by significant increase in mean energy of all frequency bands in microstate F. Combined with studies on the possible cortical sources of microstates, the findings reveal that non-canonical microstate F is highly associated with propofol-induced altered states of consciousness. The results may also support the inference that this distinct topography can be derived from canonical microstate C (anterior-posterior orientation). Finally, this study further develops pertinent methodology and extends possible applications of the EEG microstate during propofol-induced anesthesia.
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Affiliation(s)
- Wen Shi
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- School of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
| | - Yamin Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- School of Biomedical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
| | - Zhian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
| | - Jing Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- Department of Anesthesiology, Honghui Hospital, Xi’an Jiaotong University, 555 Youyi East Road, Xi’an, Shaanxi 710054, P. R. China
| | - Qiang Wang
- Department of Anesthesiology and Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, P. R. China
| | - Xiangguo Yan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
| | - Gang Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
- National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong 510500, P. R. China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, Shaanxi 710049, P. R. China
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13
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Comsa IM, Bekinschtein TA, Chennu S. Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness. Brain Topogr 2018; 32:315-331. [PMID: 30498872 PMCID: PMC6373294 DOI: 10.1007/s10548-018-0689-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2017] [Accepted: 11/22/2018] [Indexed: 12/20/2022]
Abstract
As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes. While participants performed an auditory semantic classification task, we allowed them to become drowsy and unresponsive. As they stopped responding to the stimuli, we report the breakdown of alpha networks and the emergence of theta connectivity. Further, we show that the temporal dynamics of all canonical EEG microstates slow down during unresponsiveness. We identify a specific microstate (D) whose occurrence and duration are prominently increased during this period. Employing machine learning, we show that the temporal properties of microstate D, particularly its prolonged duration, predicts the response likelihood to individual stimuli. Finally, we find a novel relationship between microstates and brain networks as we show that microstate D uniquely indexes significantly stronger theta connectivity during unresponsiveness. Our findings demonstrate that the transition to unconsciousness is not linear, but rather consists of an interplay between transient brain networks reflecting different degrees of sleep depth.
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Affiliation(s)
- Iulia M Comsa
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Srivas Chennu
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- School of Computing, University of Kent, Medway Building, Chatham Maritime, ME4 4AG, UK.
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14
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Calculation and Analysis of Microstate Related to Variation in Executed and Imagined Movement of Force of Hand Clenching. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2018:9270685. [PMID: 30224914 PMCID: PMC6129787 DOI: 10.1155/2018/9270685] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 06/20/2018] [Accepted: 07/14/2018] [Indexed: 11/17/2022]
Abstract
Objective In order to investigate electroencephalogram (EEG) instantaneous activity states related to executed and imagined movement of force of hand clenching (grip force: 4 kg, 10 kg, and 16 kg), we utilized a microstate analysis in which the spatial topographic map of EEG behaves in a certain number of discrete and stable global brain states. Approach Twenty subjects participated in EEG collection; the global field power of EEG and its local maximum were calculated and then clustered using cross validation and statistics; the 4 parameters of each microstate (duration, occurrence, time coverage, and amplitude) were calculated from the clustering results and statistically analyzed by analysis of variance (ANOVA); finally, the relationship between the microstate and frequency band was analyzed. Main Results The experimental results showed that all microstates related to executed and imagined grip force tasks were clustered into 3 microstate classes (A, B, and C); these microstates generally transitioned from A to B and then from B to C. With the increase of the target value of executed and imagined grip force, the duration and time coverage of microstate B gradually decreased, while these parameters of microstate C gradually increased. The occurrence times of microstate B and C related to executed grip force were significantly more than those related to imagined grip force; furthermore, the amplitudes of these 3 microstates related to executed grip force were significantly greater than those related to imagined grip force. The correlation coefficients between the microstates and the frequency bands indicated that the microstates were correlated to mu rhythm and beta frequency bands, which are consistent with event-related desynchronization/synchronization (ERD/ERS) phenomena of sensorimotor rhythm. Significance It is expected that this microstate analysis may be used as a new method for observing EEG instantaneous activity patterns related to variation in executed and imagined grip force and also for extracting EEG features related to these tasks. This study may lay a foundation for the application of executed and imagined grip force training for rehabilitation of hand movement disorders in patients with stroke in the future.
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15
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von Wegner F, Knaut P, Laufs H. EEG Microstate Sequences From Different Clustering Algorithms Are Information-Theoretically Invariant. Front Comput Neurosci 2018; 12:70. [PMID: 30210325 PMCID: PMC6119811 DOI: 10.3389/fncom.2018.00070] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 08/06/2018] [Indexed: 12/11/2022] Open
Abstract
We analyse statistical and information-theoretical properties of EEG microstate sequences, as seen through the lens of five different clustering algorithms. Microstate sequences are computed for n = 20 resting state EEG recordings during wakeful rest. The input for all clustering algorithms is the set of EEG topographic maps obtained at local maxima of the spatial variance. This data set is processed by two classical microstate clustering algorithms (1) atomize and agglomerate hierarchical clustering (AAHC) and (2) a modified K-means algorithm, as well as by (3) K-medoids, (4) principal component analysis (PCA) and (5) fast independent component analysis (Fast-ICA). Using this technique, EEG topographies can be substituted with microstate labels by competitive fitting based on spatial correlation, resulting in a symbolic, non-metric time series, the microstate sequence. Microstate topographies and symbolic time series are further analyzed statistically, including static and dynamic properties. Static properties, which do not contain information about temporal dependencies of the microstate sequence include the maximum similarity of microstate maps within and between the tested clustering algorithms, the global explained variance and the Shannon entropy of the microstate sequences. Dynamic properties are sensitive to temporal correlations between the symbols and include the mixing time of the microstate transition matrix, the entropy rate of the microstate sequences and the location of the first local maximum of the autoinformation function. We also test the Markov property of microstate sequences, the time stationarity of the transition matrix and detect periodicities by means of time-lagged mutual information. Finally, possible long-range correlations of microstate sequences are assessed via Hurst exponent estimation. We find that while static properties partially reflect properties of the clustering algorithms, information-theoretical quantities are largely invariant with respect to the clustering method used. As each clustering algorithm has its own profile of computational speed, ease of implementation, determinism vs. stochasticity and theoretical underpinnings, our results convey a positive message concerning the free choice of method and the comparability of results obtained from different algorithms. The invariance of these quantities implies that the tested properties are algorithm-independent, inherent features of resting state EEG derived microstate sequences.
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Affiliation(s)
- Frederic von Wegner
- Epilepsy Center Rhein-Main, Goethe University Frankfurt, Frankfurt, Germany.,Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Paul Knaut
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
| | - Helmut Laufs
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany.,Department of Neurology, University Hospital Kiel, Kiel, Germany
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16
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von Wegner F, Laufs H. Information-Theoretical Analysis of EEG Microstate Sequences in Python. Front Neuroinform 2018; 12:30. [PMID: 29910723 PMCID: PMC5992993 DOI: 10.3389/fninf.2018.00030] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 05/11/2018] [Indexed: 11/13/2022] Open
Abstract
We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG) measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography. As microstate sequences consist of non-metric random variables, e.g., the letters A-D, we recently introduced information-theoretical measures to quantify these time series. In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands. The algorithms used are here provided as an open-source package and their use is explained in a tutorial style. The package is self-contained and the programming style is procedural, focusing on code intelligibility and easy portability. Using a sample EEG file, we demonstrate how to perform EEG microstate segmentation using the modified K-means approach, and how to compute and visualize the recently introduced information-theoretical tests and quantities. The time-lagged mutual information function is derived as a discrete symbolic alternative to the autocorrelation function for metric time series and confidence intervals are computed from Markov chain surrogate data. The software package provides an open-source extension to the existing implementations of the microstate transform and is specifically designed to analyze resting state EEG recordings.
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Affiliation(s)
- Frederic von Wegner
- Epilepsy Center Rhein-Main, Goethe University Frankfurt, Frankfurt am Main, Germany.,Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Helmut Laufs
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany.,Department of Neurology, University Hospital Kiel, Kiel, Germany
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17
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Stefan S, Schorr B, Lopez-Rolon A, Kolassa IT, Shock JP, Rosenfelder M, Heck S, Bender A. Consciousness Indexing and Outcome Prediction with Resting-State EEG in Severe Disorders of Consciousness. Brain Topogr 2018; 31:848-862. [PMID: 29666960 DOI: 10.1007/s10548-018-0643-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 04/07/2018] [Indexed: 12/18/2022]
Abstract
We applied the following methods to resting-state EEG data from patients with disorders of consciousness (DOC) for consciousness indexing and outcome prediction: microstates, entropy (i.e. approximate, permutation), power in alpha and delta frequency bands, and connectivity (i.e. weighted symbolic mutual information, symbolic transfer entropy, complex network analysis). Patients with unresponsive wakefulness syndrome (UWS) and patients in a minimally conscious state (MCS) were classified into these two categories by fitting and testing a generalised linear model. We aimed subsequently to develop an automated system for outcome prediction in severe DOC by selecting an optimal subset of features using sequential floating forward selection (SFFS). The two outcome categories were defined as UWS or dead, and MCS or emerged from MCS. Percentage of time spent in microstate D in the alpha frequency band performed best at distinguishing MCS from UWS patients. The average clustering coefficient obtained from thresholding beta coherence performed best at predicting outcome. The optimal subset of features selected with SFFS consisted of the frequency of microstate A in the 2-20 Hz frequency band, path length obtained from thresholding alpha coherence, and average path length obtained from thresholding alpha coherence. Combining these features seemed to afford high prediction power. Python and MATLAB toolboxes for the above calculations are freely available under the GNU public license for non-commercial use ( https://qeeg.wordpress.com ).
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Affiliation(s)
- Sabina Stefan
- School of Engineering, Brown University, 182 Hope Street, Box D, Providence, RI, 02912, USA
| | - Barbara Schorr
- Department of Neurology, Therapiezentrum Burgau, Kapuzinerstrasse 34, 89331, Burgau, Germany.,Clinical and Biological Psychology, Institute of Psychology and Education, Ulm University, 89081, Ulm, Germany
| | - Alex Lopez-Rolon
- Department of Neurology, University of Munich, Marchioninistrasse 15, 81377, Munich, Germany
| | - Iris-Tatjana Kolassa
- Clinical and Biological Psychology, Institute of Psychology and Education, Ulm University, 89081, Ulm, Germany
| | - Jonathan P Shock
- Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch, Private Bag X1, Cape Town, 7701, South Africa.
| | - Martin Rosenfelder
- Department of Neurology, Therapiezentrum Burgau, Kapuzinerstrasse 34, 89331, Burgau, Germany.,Clinical and Biological Psychology, Institute of Psychology and Education, Ulm University, 89081, Ulm, Germany
| | - Suzette Heck
- Department of Neurology, University of Munich, Marchioninistrasse 15, 81377, Munich, Germany
| | - Andreas Bender
- Department of Neurology, Therapiezentrum Burgau, Kapuzinerstrasse 34, 89331, Burgau, Germany.,Department of Neurology, University of Munich, Marchioninistrasse 15, 81377, Munich, Germany
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18
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Wang J, Zhang Y, Ma Q. Analysis of Narcolepsy Based on Single-Channel EEG Signals. BIG DATA ANALYTICS 2018. [DOI: 10.1007/978-3-030-04780-1_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
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19
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von Wegner F, Tagliazucchi E, Laufs H. Information-theoretical analysis of resting state EEG microstate sequences - non-Markovianity, non-stationarity and periodicities. Neuroimage 2017; 158:99-111. [PMID: 28673879 DOI: 10.1016/j.neuroimage.2017.06.062] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Revised: 06/19/2017] [Accepted: 06/22/2017] [Indexed: 01/28/2023] Open
Abstract
We present an information-theoretical analysis of temporal dependencies in EEG microstate sequences during wakeful rest. We interpret microstate sequences as discrete stochastic processes where each state corresponds to a representative scalp potential topography. Testing low-order Markovianity of these discrete sequences directly, we find that none of the recordings fulfils the Markov property of order 0, 1 or 2. Further analyses show that the microstate transition matrix is non-stationary over time in 80% (window size 10 s), 60% (window size 20 s) and 44% (window size 40 s) of the subjects, and that transition matrices are asymmetric in 14/20 (70%) subjects. To assess temporal dependencies globally, the time-lagged mutual information function (autoinformation function) of each sequence is compared to the first-order Markov model defined by the classical transition matrix approach. The autoinformation function for the Markovian case is derived analytically and numerically. For experimental data, we find non-Markovian behaviour in the range of the main EEG frequency bands where distinct periodicities related to the subject's EEG frequency spectrum appear. In particular, the microstate clustering algorithm induces frequency doubling with respect to the EEG power spectral density while the tail of the autoinformation function asymptotically reaches the first-order Markov confidence interval for time lags above 1000 ms. In summary, our results show that resting state microstate sequences are non-Markovian processes which inherit periodicities from the underlying EEG dynamics. Our results interpolate between two diverging models of microstate dynamics, memoryless Markov models on one side, and long-range correlated models on the other: microstate sequences display more complex temporal dependencies than captured by the transition matrix approach in the range of the main EEG frequency bands, but show finite memory content in the long run.
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Affiliation(s)
- F von Wegner
- Epilepsy Center Rhein-Main, Goethe University Frankfurt, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany; Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany.
| | - E Tagliazucchi
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany; Department of Neurology, Christian-Albrechts University Kiel, Arnold-Heller-Strasse 3, 24105 Kiel, Germany
| | - H Laufs
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany; Department of Neurology, Christian-Albrechts University Kiel, Arnold-Heller-Strasse 3, 24105 Kiel, Germany
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20
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Zhang X, Kantelhardt JW, Dong XS, Krefting D, Li J, Yan H, Pillmann F, Fietze I, Penzel T, Zhao L, Han F. Nocturnal Dynamics of Sleep–Wake Transitions in Patients With Narcolepsy. Sleep 2016; 40:2740618. [DOI: 10.1093/sleep/zsw050] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2016] [Indexed: 11/13/2022] Open
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21
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Drissi NM, Szakács A, Witt ST, Wretman A, Ulander M, Ståhlbrandt H, Darin N, Hallböök T, Landtblom AM, Engström M. Altered Brain Microstate Dynamics in Adolescents with Narcolepsy. Front Hum Neurosci 2016; 10:369. [PMID: 27536225 PMCID: PMC4971065 DOI: 10.3389/fnhum.2016.00369] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 07/11/2016] [Indexed: 11/13/2022] Open
Abstract
Narcolepsy is a chronic sleep disorder caused by a loss of hypocretin-1 producing neurons in the hypothalamus. Previous neuroimaging studies have investigated brain function in narcolepsy during rest using positron emission tomography (PET) and single photon emission computed tomography (SPECT). In addition to hypothalamic and thalamic dysfunction they showed aberrant prefrontal perfusion and glucose metabolism in narcolepsy. Given these findings in brain structure and metabolism in narcolepsy, we anticipated that changes in functional magnetic resonance imaging (fMRI) resting state network (RSN) dynamics might also be apparent in patients with narcolepsy. The objective of this study was to investigate and describe brain microstate activity in adolescents with narcolepsy and correlate these to RSNs using simultaneous fMRI and electroencephalography (EEG). Sixteen adolescents (ages 13-20) with a confirmed diagnosis of narcolepsy were recruited and compared to age-matched healthy controls. Simultaneous EEG and fMRI data were collected during 10 min of wakeful rest. EEG data were analyzed for microstates, which are discrete epochs of stable global brain states obtained from topographical EEG analysis. Functional MRI data were analyzed for RSNs. Data showed that narcolepsy patients were less likely than controls to spend time in a microstate which we found to be related to the default mode network and may suggest a disruption of this network that is disease specific. We concluded that adolescents with narcolepsy have altered resting state brain dynamics.
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Affiliation(s)
- Natasha M Drissi
- Department of Medical and Health Sciences (IMH), Linköping UniversityLinköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping UniversityLinköping, Sweden
| | - Attila Szakács
- Department of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg Gothenburg, Sweden
| | - Suzanne T Witt
- Center for Medical Image Science and Visualization (CMIV), Linköping University Linköping, Sweden
| | - Anna Wretman
- Department of Behavioral Science and Learning, Linköping University Linköping, Sweden
| | - Martin Ulander
- Department of Clinical and Experimental Medicine, Linköping University Linköping, Sweden
| | | | - Niklas Darin
- Department of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg Gothenburg, Sweden
| | - Tove Hallböök
- Department of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg Gothenburg, Sweden
| | - Anne-Marie Landtblom
- Department of Clinical and Experimental Medicine, Linköping UniversityLinköping, Sweden; Department of Neurology, Uppsala UniversityUppsala, Sweden
| | - Maria Engström
- Department of Medical and Health Sciences (IMH), Linköping UniversityLinköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping UniversityLinköping, Sweden
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22
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von Wegner F, Tagliazucchi E, Brodbeck V, Laufs H. Analytical and empirical fluctuation functions of the EEG microstate random walk - Short-range vs. long-range correlations. Neuroimage 2016; 141:442-451. [PMID: 27485754 DOI: 10.1016/j.neuroimage.2016.07.050] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Revised: 07/22/2016] [Accepted: 07/25/2016] [Indexed: 01/22/2023] Open
Abstract
We analyze temporal autocorrelations and the scaling behaviour of EEG microstate sequences during wakeful rest. We use the recently introduced random walk approach and compute its fluctuation function analytically under the null hypothesis of a short-range correlated, first-order Markov process. The empirical fluctuation function and the Hurst parameter H as a surrogate parameter of long-range correlations are computed from 32 resting state EEG recordings and for a set of first-order Markov surrogate data sets with equilibrium distribution and transition matrices identical to the empirical data. In order to distinguish short-range correlations (H ≈ 0.5) from previously reported long-range correlations (H > 0.5) statistically, confidence intervals for H and the fluctuation functions are constructed under the null hypothesis. Comparing three different estimation methods for H, we find that only one data set consistently shows H > 0.5, compatible with long-range correlations, whereas the majority of experimental data sets cannot be consistently distinguished from Markovian scaling behaviour. Our analysis suggests that the scaling behaviour of resting state EEG microstate sequences, though markedly different from uncorrelated, zero-order Markov processes, can often not be distinguished from a short-range correlated, first-order Markov process. Our results do not prove the microstate process to be Markovian, but challenge the approach to parametrize resting state EEG by single parameter models.
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Affiliation(s)
- F von Wegner
- Epilepsy Center Rhein-Main, Goethe University Frankfurt am Main, Schleusenweg 2-16, Frankfurt am Main 60528, Germany; Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am Main, Schleusenweg 2-16, Frankfurt am Main 60528, Germany.
| | - E Tagliazucchi
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am Main, Schleusenweg 2-16, Frankfurt am Main 60528, Germany; Department of Neurology, University Hospital Kiel, Schittenhelmstrasse 10, Kiel 24105, Germany
| | - V Brodbeck
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am Main, Schleusenweg 2-16, Frankfurt am Main 60528, Germany
| | - H Laufs
- Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am Main, Schleusenweg 2-16, Frankfurt am Main 60528, Germany; Department of Neurology, University Hospital Kiel, Schittenhelmstrasse 10, Kiel 24105, Germany
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