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Varley TF, Sporns O. Network Analysis of Time Series: Novel Approaches to Network Neuroscience. Front Neurosci 2022; 15:787068. [PMID: 35221887 PMCID: PMC8874015 DOI: 10.3389/fnins.2021.787068] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/20/2021] [Indexed: 12/12/2022] Open
Abstract
In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.
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Affiliation(s)
- Thomas F. Varley
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, United States
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
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Darracq M, Sleigh J, Banks MI, Sanders RD. Characterising the effect of propofol on complexity and stability in the EEG power spectrum. Br J Anaesth 2018; 121:1368-1369. [PMID: 30442267 DOI: 10.1016/j.bja.2018.09.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 08/24/2018] [Accepted: 09/10/2018] [Indexed: 10/28/2022] Open
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Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation. BIOMED RESEARCH INTERNATIONAL 2015; 2015:830926. [PMID: 26380297 PMCID: PMC4561308 DOI: 10.1155/2015/830926] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2015] [Accepted: 02/18/2015] [Indexed: 11/25/2022]
Abstract
This paper presents a data-driven multiscale entropy measure to reveal the scale dependent information quantity of electroencephalogram (EEG) recordings. This work is motivated by the previous observations on the nonlinear and nonstationary nature of EEG over multiple time scales. Here, a new framework of entropy measures considering changing dynamics over multiple oscillatory scales is presented. First, to deal with nonstationarity over multiple scales, EEG recording is decomposed by applying the empirical mode decomposition (EMD) which is known to be effective for extracting the constituent narrowband components without a predetermined basis. Following calculation of Renyi entropy of the probability distributions of the intrinsic mode functions extracted by EMD leads to a data-driven multiscale Renyi entropy. To validate the performance of the proposed entropy measure, actual EEG recordings from rats (n = 9) experiencing 7 min cardiac arrest followed by resuscitation were analyzed. Simulation and experimental results demonstrate that the use of the multiscale Renyi entropy leads to better discriminative capability of the injury levels and improved correlations with the neurological deficit evaluation after 72 hours after cardiac arrest, thus suggesting an effective diagnostic and prognostic tool.
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Li TN, Li Y. Depth of anaesthesia monitors and the latest algorithms. ASIAN PAC J TROP MED 2014; 7:429-37. [DOI: 10.1016/s1995-7645(14)60070-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 03/15/2014] [Accepted: 04/15/2014] [Indexed: 10/25/2022] Open
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Carrubba S, Minagar A, Chesson AL, Frilot C, Marino AA. Increased determinism in brain electrical activity occurs in association with multiple sclerosis. Neurol Res 2013; 34:286-90. [DOI: 10.1179/1743132812y.0000000010] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Affiliation(s)
- Simona Carrubba
- Natural Sciences DepartmentDaemen College, Amherst, New York, USA
| | | | | | - Clifton Frilot
- School of Allied Health Professions, Louisiana State University Health Sciences Center, Shreveport, USA
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Li D, Liang Z, Wang Y, Hagihira S, Sleigh JW, Li X. Parameter selection in permutation entropy for an electroencephalographic measure of isoflurane anesthetic drug effect. J Clin Monit Comput 2012; 27:113-23. [PMID: 23264067 DOI: 10.1007/s10877-012-9419-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2012] [Accepted: 12/02/2012] [Indexed: 10/27/2022]
Abstract
The permutation entropy (PE) of the electroencephalographic (EEG) signals has been proposed as a robust measure of anesthetic drug effect. The calculation of PE involves the somewhat arbitrary selection of embedding dimension (m) and lag (τ) parameters. Previous studies of PE include the analysis of EEG signals under sevoflurane or propofol anesthesia, where different parameter settings were determined using a number of different criteria. In this study we choose parameter values based on the quantitative performance, to quantify the effect of a wide range of concentrations of isoflurane on the EEG. We analyzed a set of previously published EEG data, obtained from 29 patients who underwent elective abdominal surgery under isoflurane general anesthesia combined with epidural anesthesia. PE indices using a range of different parameter settings (m = 3-7, τ = 1-5) were calculated. These indices were evaluated as regards: the correlation coefficient (r) with isoflurane end-tidal concentration, the relationship with isoflurane effect-site concentration assessed by the coefficient of determination (R (2)) of the pharmacokinetic-pharmacodynamic models, and the prediction probability (PK). The embedding dimension (m) and lag (τ) have significant effect on the r values (Two-way repeated-measures ANOVA, p < 0.001). The proposed new permutation entropy index (NPEI) [a combination of PE(m = 3, τ = 2) and PE(m = 3, τ = 3)] performed best among all the parameter combinations, with r = 0.89(0.83-0.94), R (2) = 0.82(0.76-0.87), and PK = 0.80 (0.76-0.85). Further comparison with previously suggested PE measures, as well as other unrelated EEG measures, indicates the superiority of the NPEI. The PE can be utilized to indicate the dynamical changes of EEG signals under isoflurane anesthesia. In this study, the NPEI measure that combines the PE with m = 3, τ = 2 and that with m = 3, τ = 3 is optimal.
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Affiliation(s)
- Duan Li
- Institute of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China
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SHALBAF R, BEHNAM H, SLEIGH J, VOSS L. Measuring the effects of sevoflurane on electroencephalogram using sample entropy. Acta Anaesthesiol Scand 2012; 56:880-9. [PMID: 22404496 DOI: 10.1111/j.1399-6576.2012.02676.x] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/06/2012] [Indexed: 11/30/2022]
Abstract
BACKGROUND Monitoring the effect of anesthetic drugs on the neural system is a major ongoing challenge for anesthetists. During the past few years, several electroencephalogram (EEG)-based methods such as the response entropy (RE) as implemented in the Datex-Ohmeda M-Entropy Module have been proposed. In this paper, sample entropy is used to quantify the predictability of EEG series, which could provide an index to show the effect of sevoflurane anesthesia. The dose-response relation of sample entropy is compared with that of RE. METHODS EEG data from 21 subjects is collected during the induction of general anesthesia with sevoflurane. The sample entropy is applied to the EEG recording. Pharmacokinetic-pharmacodynamic modeling and prediction probability statistic are used to evaluate the efficiency of sample entropy in comparison with RE. RESULTS Both methods track the gross changes in EEG, especially the occurrence of burst-suppression pattern at high doses of anesthetics. However, our method produces faster reaction to transients in EEG during the induction of anesthesia as indicated from the pharmacokinetic and pharmacodynamic modeled parameters and analysis around the point of loss of consciousness. Also, sample entropy correlated more closely with effect-site sevoflurane concentration than the RE. In addition, our proposed method exhibits greater resistance to noise in the EEG signals. CONCLUSION The results demonstrate that sample entropy can estimate the sevoflurane drug effect on the EEG more effectively than the commercial RE index with a stronger noise resistance.
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Affiliation(s)
- R. SHALBAF
- School of Electrical Engineering; Iran University of Science & Technology; Tehran; Iran
| | - H. BEHNAM
- School of Electrical Engineering; Iran University of Science & Technology; Tehran; Iran
| | - J. SLEIGH
- Department of Anesthesia; Waikato Hospital; Hamilton; New Zealand
| | - L. VOSS
- Department of Anesthesia; Waikato Hospital; Hamilton; New Zealand
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Chen D, Li D, Xiong M, Bao H, Li X. GPGPU-aided ensemble empirical-mode decomposition for EEG analysis during anesthesia. ACTA ACUST UNITED AC 2010; 14:1417-27. [PMID: 20813649 DOI: 10.1109/titb.2010.2072963] [Citation(s) in RCA: 89] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Ensemble empirical-mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. Unfortunately, since the EEMD is highly compute-intensive, the method does not apply in real-time applications on top of commercial-off-the-shelf computers. Aiming at this problem, a parallelized EEMD method has been developed using general-purpose computing on the graphics processing unit (GPGPU), namely, G-EEMD. A spectral entropy facilitated by G-EEMD was, therefore, proposed to analyze the EEG data for estimating the depth of anesthesia (DoA) in a real-time manner. In terms of EEG data analysis, G-EEMD has dramatically improved the run-time performance by more than 140 times compared to the original serial EEMD implementation. G-EEMD also performs far better than another parallelized implementation of EEMD bases on conventional CPU-based distributed computing technology despite the latter utilizes 16 high-end computing nodes for the same computing task. Furthermore, the results obtained from a pharmacokinetics/pharmacodynamic (PK/PD) model analysis indicate that the EEMD method is slightly more effective than its precedent alternative method (EMD) in estimating DoA, the coefficient of determination R(2) by EEMD is significantly higher than that by EMD (p < 0.05, paired t-test) and the prediction probability P(k) by EEMD is also slighter higher than that by EMD (p < 0.2, paired t-test).
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Affiliation(s)
- Dan Chen
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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Li D, Li X, Liang Z, Voss LJ, Sleigh JW. Multiscale permutation entropy analysis of EEG recordings during sevoflurane anesthesia. J Neural Eng 2010; 7:046010. [DOI: 10.1088/1741-2560/7/4/046010] [Citation(s) in RCA: 112] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Becker K, Schneider G, Eder M, Ranft A, Kochs EF, Zieglgänsberger W, Dodt HU. Anaesthesia monitoring by recurrence quantification analysis of EEG data. PLoS One 2010; 5:e8876. [PMID: 20126649 PMCID: PMC2811188 DOI: 10.1371/journal.pone.0008876] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2009] [Accepted: 01/04/2010] [Indexed: 11/18/2022] Open
Abstract
Appropriate monitoring of the depth of anaesthesia is crucial to prevent deleterious effects of insufficient anaesthesia on surgical patients. Since cardiovascular parameters and motor response testing may fail to display awareness during surgery, attempts are made to utilise alterations in brain activity as reliable markers of the anaesthetic state. Here we present a novel, promising approach for anaesthesia monitoring, basing on recurrence quantification analysis (RQA) of EEG recordings. This nonlinear time series analysis technique separates consciousness from unconsciousness during both remifentanil/sevoflurane and remifentanil/propofol anaesthesia with an overall prediction probability of more than 85%, when applied to spontaneous one-channel EEG activity in surgical patients.
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Affiliation(s)
- Klaus Becker
- Bioelectronics, Vienna University of Technology, Vienna, Austria.
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Cong F, Sipola T, Huttunen-Scott T, Xu X, Ristaniemi T, Lyytinen H. Hilbert-Huang versus Morlet wavelet transformation on mismatch negativity of children in uninterrupted sound paradigm. NONLINEAR BIOMEDICAL PHYSICS 2009; 3:1. [PMID: 19187527 PMCID: PMC2654895 DOI: 10.1186/1753-4631-3-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2008] [Accepted: 02/02/2009] [Indexed: 05/27/2023]
Abstract
BACKGROUND Compared to the waveform or spectrum analysis of event-related potentials (ERPs), time-frequency representation (TFR) has the advantage of revealing the ERPs time and frequency domain information simultaneously. As the human brain could be modeled as a complicated nonlinear system, it is interesting from the view of psychological knowledge to study the performance of the nonlinear and linear time-frequency representation methods for ERP research. In this study Hilbert-Huang transformation (HHT) and Morlet wavelet transformation (MWT) were performed on mismatch negativity (MMN) of children. Participants were 102 children aged 8-16 years. MMN was elicited in a passive oddball paradigm with duration deviants. The stimuli consisted of an uninterrupted sound including two alternating 100 ms tones (600 and 800 Hz) with infrequent 50 ms or 30 ms 600 Hz deviant tones. In theory larger deviant should elicit larger MMN. This theoretical expectation is used as a criterion to test two TFR methods in this study. For statistical analysis MMN support to absence ratio (SAR) could be utilized to qualify TFR of MMN. RESULTS Compared to MWT, the TFR of MMN with HHT was much sharper, sparser, and clearer. Statistically, SAR showed significant difference between the MMNs elicited by two deviants with HHT but not with MWT, and the larger deviant elicited MMN with larger SAR. CONCLUSION Support to absence ratio of Hilbert-Huang Transformation on mismatch negativity meets the theoretical expectations, i.e., the more deviant stimulus elicits larger MMN. However, Morlet wavelet transformation does not reveal that. Thus, HHT seems more appropriate in analyzing event-related potentials in the time-frequency domain. HHT appears to evaluate ERPs more accurately and provide theoretically valid information of the brain responses.
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Affiliation(s)
- Fengyu Cong
- Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Tuomo Sipola
- Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | | | - Xiaonan Xu
- Hangzhou Applied Acoustic Institute, Hangzhou, PR China
| | - Tapani Ristaniemi
- Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Heikki Lyytinen
- Department of Psychology, University of Jyväskylä, Jyväskylä, Finland
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Li X, Li D, Liang Z, Voss LJ, Sleigh JW. Analysis of depth of anesthesia with Hilbert–Huang spectral entropy. Clin Neurophysiol 2008; 119:2465-75. [PMID: 18812265 DOI: 10.1016/j.clinph.2008.08.006] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2008] [Revised: 08/07/2008] [Accepted: 08/13/2008] [Indexed: 11/17/2022]
Affiliation(s)
- Xiaoli Li
- Key Lab of Industrial Computer Control Engineering of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
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Ouyang G, Li X, Dang C, Richards DA. Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats. Clin Neurophysiol 2008; 119:1747-1755. [PMID: 18486542 DOI: 10.1016/j.clinph.2008.04.005] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2008] [Revised: 03/28/2008] [Accepted: 04/01/2008] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Understanding the transition of brain activity towards an absence seizure is a challenging task. In this paper, we use recurrence quantification analysis to indicate the deterministic dynamics of EEG series at the seizure-free, pre-seizure and seizure states in genetic absence epilepsy rats. METHODS The determinism measure, DET, based on recurrence plot, was applied to analyse these three EEG datasets, each dataset containing 300 single-channel EEG epochs of 5-s duration. Then, statistical analysis of the DET values in each dataset was carried out to determine whether their distributions over the three groups were significantly different. Furthermore, a surrogate technique was applied to calculate the significance level of determinism measures in EEG recordings. RESULTS The mean (+/-SD) DET of EEG was 0.177+/-0.045 in pre-seizure intervals. The DET values of pre-seizure EEG data are significantly higher than those of seizure-free intervals, 0.123+/-0.023, (P<0.01), but lower than those of seizure intervals, 0.392+/-0.110, (P<0.01). Using surrogate data methods, the significance of determinism in EEG epochs was present in 25 of 300 (8.3%), 181 of 300 (60.3%) and 289 of 300 (96.3%) in seizure-free, pre-seizure and seizure intervals, respectively. CONCLUSIONS Results provide some first indications that EEG epochs during pre-seizure intervals exhibit a higher degree of determinism than seizure-free EEG epochs, but lower than those in seizure EEG epochs in absence epilepsy. SIGNIFICANCE The proposed methods have the potential of detecting the transition between normal brain activity and the absence seizure state, thus opening up the possibility of intervention, whether electrical or pharmacological, to prevent the oncoming seizure.
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Affiliation(s)
- Gaoxiang Ouyang
- Department of MEEM, City University of Hong Kong, Kowloon, Hong Kong
| | - Xiaoli Li
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
| | - Chuangyin Dang
- Department of MEEM, City University of Hong Kong, Kowloon, Hong Kong
| | - Douglas A Richards
- Department of Pharmacology, Division of Neuroscience, The Medical School, The University of Birmingham, B15 2TT, UK
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Li X, Cui D, Jiruska P, Fox JE, Yao X, Jefferys JGR. Synchronization Measurement of Multiple Neuronal Populations. J Neurophysiol 2007; 98:3341-8. [PMID: 17913983 DOI: 10.1152/jn.00977.2007] [Citation(s) in RCA: 62] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The purpose of the present paper is to develop a method, based on equal-time correlation, correlation matrix analysis and surrogate resampling, that is able to quantify and describe properties of synchronization of population neuronal activity recorded simultaneously from multiple sites. Initially, Lorenz-type oscillators were used to model multiple time series with different patterns of synchronization. Eigenvalue and eigenvector decomposition was then applied to identify “clusters” of locally synchronized activity and to calculate a “global synchronization index.” This method was then applied to multichannel data recorded from an in vitro model of epileptic seizures. The results demonstrate that this novel method can be successfully used to analyze synchronization between multiple neuronal population series.
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Affiliation(s)
- Xiaoli Li
- The Centre of Excellence for Research in Computational Intelligence and Applications, School of Computer Science, The University of Birmingham, Birmingham, UK.
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