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Fotiadis A, Vlachos I, Kugiumtzis D. The causality measure of partial mutual information from mixed embedding (PMIME) revisited. CHAOS (WOODBURY, N.Y.) 2024; 34:033113. [PMID: 38447936 DOI: 10.1063/5.0189056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 02/14/2024] [Indexed: 03/08/2024]
Abstract
The measure of partial mutual information from mixed embedding (PMIME) is an information theory-based measure to accurately identify the direct and directional coupling, termed Granger causality or simply causality, between the observed variables or subsystems of a high-dimensional dynamical and complex system, without any a priori assumptions about the nature of the coupling relationship. In its core, it is a forward selection procedure that aims to iteratively identify the lag-dependence structure of a given observed variable (response) to all the other observed variables (candidate drivers). This model-free approach is capable of detecting nonlinear interactions, abundantly present in real-world complex systems, and it was shown to perform well on multivariate time series of moderately high dimension. However, the PMIME presents some inefficiencies in its performance mainly when applied on strongly stochastic (linear or nonlinear) systems as it may falsely detect non-existent relationships. Moreover, and by construction, the measure cannot extract purely synergetic relationships present in a system. In the current work, the issue of false detections is addressed by introducing an improved resampling significance test and a procedure of rechecking the identified drivers (backward revision). Regarding the inability to detect synergetic relationships, the PMIME is further enhanced by checking pairs as candidate drivers for the response variable after having considered all drivers individually. The effects of these modifications are investigated in a systematic simulation study on properly designed systems involving strong stochasticity, regressor terms with synergetic effects, and a system dimension ranging from 3 to 30. The overall results of the simulations indicate that these modifications indeed improve the performance of PMIME and alleviate to a significant degree the issues of the original algorithm. Guidelines for balancing between accuracy and computational efficiency are also given, particularly relevant for real-world applications. Finally, the measure performance is investigated in the study of futures of various government bonds and stock market indices in the period around COVID-19 pandemic.
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Affiliation(s)
- Akylas Fotiadis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Ioannis Vlachos
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Pod Vodárenskou věží 2, 182 07 Prague 8, Czech Republic
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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2
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Dervinis M, Crunelli V. Spike-and-wave discharges of absence seizures in a sleep waves-constrained corticothalamic model. CNS Neurosci Ther 2024; 30:e14204. [PMID: 37032628 PMCID: PMC10915988 DOI: 10.1111/cns.14204] [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: 11/02/2022] [Revised: 03/18/2023] [Accepted: 03/24/2023] [Indexed: 04/11/2023] Open
Abstract
AIMS Recurrent network activity in corticothalamic circuits generates physiological and pathological EEG waves. Many computer models have simulated spike-and-wave discharges (SWDs), the EEG hallmark of absence seizures (ASs). However, these models either provided detailed simulated activity only in a selected territory (i.e., cortical or thalamic) or did not test whether their corticothalamic networks could reproduce the physiological activities that are generated by these circuits. METHODS Using a biophysical large-scale corticothalamic model that reproduces the full extent of EEG sleep waves, including sleep spindles, delta, and slow (<1 Hz) waves, here we investigated how single abnormalities in voltage- or transmitter-gated channels in the neocortex or thalamus led to SWDs. RESULTS We found that a selective increase in the tonic γ-aminobutyric acid type A receptor (GABA-A) inhibition of first-order thalamocortical (TC) neurons or a selective decrease in cortical phasic GABA-A inhibition is sufficient to generate ~4 Hz SWDs (as in humans) that invariably start in neocortical territories. Decreasing the leak conductance of higher-order TC neurons leads to ~7 Hz SWDs (as in rodent models) while maintaining sleep spindles at 7-14 Hz. CONCLUSION By challenging key features of current mechanistic views, this simulated ictal corticothalamic activity provides novel understanding of ASs and makes key testable predictions.
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Affiliation(s)
- Martynas Dervinis
- Neuroscience Division, School of BioscienceCardiff UniversityMuseum AvenueCardiffCF10 3AXUK
- Present address:
School of Physiology, Pharmacology and NeuroscienceBiomedical BuildingBristolBS8 1TDUK
| | - Vincenzo Crunelli
- Neuroscience Division, School of BioscienceCardiff UniversityMuseum AvenueCardiffCF10 3AXUK
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3
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Weiss DA, Borsa AM, Pala A, Sederberg AJ, Stanley GB. A machine learning approach for real-time cortical state estimation. J Neural Eng 2024; 21:016016. [PMID: 38232377 PMCID: PMC10868597 DOI: 10.1088/1741-2552/ad1f7b] [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: 06/20/2023] [Accepted: 01/17/2024] [Indexed: 01/19/2024]
Abstract
Objective.Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as 'cortical state'. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation.Approach. We use unsupervised Gaussian mixture models to identify discrete, emergent clusters in spontaneous local field potential signals in cortex. We then extend our approach to a temporally-informed hidden semi-Markov model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments.Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data.Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.
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Affiliation(s)
- David A Weiss
- Program in Bioengineering, Georgia Institute of Technology, Atlanta, GA, United States of America
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States of America
| | - Adriano Mf Borsa
- Program in Bioengineering, Georgia Institute of Technology, Atlanta, GA, United States of America
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Aurélie Pala
- Department of Biology, Emory University, Atlanta, GA, United States of America
| | - Audrey J Sederberg
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, MN, United States of America
- Medical Discovery Team in Optical Imaging and Brain Science, University of Minnesota, Minneapolis, MN, United States of America
| | - Garrett B Stanley
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States of America
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Xu Z, Tang S, Liu C, Zhang Q, Gu H, Li X, Di Z, Li Z. Temporal segmentation of EEG based on functional connectivity network structure. Sci Rep 2023; 13:22566. [PMID: 38114604 PMCID: PMC10730570 DOI: 10.1038/s41598-023-49891-8] [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: 07/26/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
In the study of brain functional connectivity networks, it is assumed that a network is built from a data window in which activity is stationary. However, brain activity is non-stationary over sufficiently large time periods. Addressing the analysis electroencephalograph (EEG) data, we propose a data segmentation method based on functional connectivity network structure. The goal of segmentation is to ensure that within a window of analysis, there is similar network structure. We designed an intuitive and flexible graph distance measure to quantify the difference in network structure between two analysis windows. This measure is modular: a variety of node importance indices can be plugged into it. We use a reference window versus sliding window comparison approach to detect changes, as indicated by outliers in the distribution of graph distance values. Performance of our segmentation method was tested in simulated EEG data and real EEG data from a drone piloting experiment (using correlation or phase-locking value as the functional connectivity strength metric). We compared our method under various node importance measures and against matrix-based dissimilarity metrics that use singular value decomposition on the connectivity matrix. The results show the graph distance approach worked better than matrix-based approaches; graph distance based on partial node centrality was most sensitive to network structural changes, especially when connectivity matrix values change little. The proposed method provides EEG data segmentation tailored for detecting changes in terms of functional connectivity networks. Our study provides a new perspective on EEG segmentation, one that is based on functional connectivity network structure differences.
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Affiliation(s)
- Zhongming Xu
- The International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, China
- The Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, 519087, China
- The School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Shaohua Tang
- The Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, 519087, China
- The School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Chuancai Liu
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Qiankun Zhang
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Heng Gu
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Xiaoli Li
- The State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Zengru Di
- The International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, China
| | - Zheng Li
- The Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, 519087, China.
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5
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Letellier C, Sendiña-Nadal I, Leyva I, Barbot JP. Generalized synchronization mediated by a flat coupling between structurally nonequivalent chaotic systems. CHAOS (WOODBURY, N.Y.) 2023; 33:093117. [PMID: 37703476 DOI: 10.1063/5.0156025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/14/2023] [Indexed: 09/15/2023]
Abstract
Synchronization of chaotic systems is usually investigated for structurally equivalent systems typically coupled through linear diffusive functions. Here, we focus on a particular type of coupling borrowed from a nonlinear control theory and based on the optimal placement of a sensor-a device measuring the chosen variable-and an actuator-a device applying the actuating (control) signal to a variable's derivative-in the response system, leading to the so-called flat control law. We aim to investigate the dynamics produced by a response system that is flat coupled to a drive system and to determine the degree of generalized synchronization between them using statistical and topological arguments. The general use of a flat control law for getting generalized synchronization is discussed.
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Affiliation(s)
- Christophe Letellier
- Rouen Normandie University-CORIA, Avenue de l'Université, F-76800 Saint-Etienne du Rouvray, France
| | - Irene Sendiña-Nadal
- Complex Systems Group & GISC, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
- Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - I Leyva
- Complex Systems Group & GISC, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
- Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain
| | - Jean-Pierre Barbot
- QUARTZ EA7393 Laboratory, ENSEA, 6 Avenue du Ponceau, 95014 Cergy-Pontoise, France
- LS2N, UMR 6004 CNRS, École Centrale de Nantes, 1 rue de la Noë, 44300 Nantes, France
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6
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Kose MR, Ahirwal MK, Atulkar M. Weighted ordinal connection based functional network classification for schizophrenia disease detection using EEG signal. Phys Eng Sci Med 2023; 46:1055-1070. [PMID: 37222953 DOI: 10.1007/s13246-023-01273-0] [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: 07/25/2022] [Accepted: 05/02/2023] [Indexed: 05/25/2023]
Abstract
A brain connectivity network (BCN) is an advanced approach to examining brain functionality in various conditions. However, the predictability of the BCN is affected by the connectivity measure used for the network construction. Various connectivity measures available in the literature differ according to the domain of their working data. The application of random connectivity measures might result in an inefficient BCN that ultimately hampers its predictability. Therefore, selecting an appropriate functional connectivity metric is crucial in clinical as well as cognitive neuroscience. In parallel to this, an effective network identifier plays a vital role in distinguishing different brain states. Hence, the objective of this paper is two-fold, which includes identifying suitable connectivity measures and proposing an efficient network identifier. For this, the weighted BCN (WBCN) is constructed using multiple connectivity measures like correlation coefficient (r), coherence (COH), phase-locking value (PLV), and mutual information (MI) from electroencephalogram (EEG) signals. The most recent technique for feature extraction, i.e., weighted ordinal connections, has been applied to EEG-based BCN. EEG signals data has been taken from the schizophrenia disease database. Further, several classification algorithms such as k-nearest neighbours (KNN), support vector machine (SVM) with linear, radial basis function and polynomial kernels, random forest (RF), and 1D convolutional neural network (CNN1D) are used to classify the brain states based on extracted features. In classification, 90% accuracy is achieved by the CNN1D classifier with WBCN based on the coherence connectivity measure. The study also provides a structural analysis of the BCN.
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Affiliation(s)
- Mangesh R Kose
- Department of Computer Application, NIT, Raipur, 492010, CG, India.
| | - Mitul K Ahirwal
- Department of Computer Science and Engineering, MANIT, Bhopal, 462003, MP, India
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7
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Miwakeichi F, Galka A. Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1070. [PMID: 37510017 PMCID: PMC10378223 DOI: 10.3390/e25071070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 07/14/2023] [Accepted: 07/16/2023] [Indexed: 07/30/2023]
Abstract
In this study, we present a thorough comparison of the performance of four different bootstrap methods for assessing the significance of causal analysis in time series data. For this purpose, multivariate simulated data are generated by a linear feedback system. The methods investigated are uncorrelated Phase Randomization Bootstrap (uPRB), which generates surrogate data with no cross-correlation between variables by randomizing the phase in the frequency domain; Time Shift Bootstrap (TSB), which generates surrogate data by randomizing the phase in the time domain; Stationary Bootstrap (SB), which calculates standard errors and constructs confidence regions for weakly dependent stationary observations; and AR-Sieve Bootstrap (ARSB), a resampling method based on AutoRegressive (AR) models that approximates the underlying data-generating process. The uPRB method accurately identifies variable interactions but fails to detect self-feedback in some variables. The TSB method, despite performing worse than uPRB, is unable to detect feedback between certain variables. The SB method gives consistent causality results, although its ability to detect self-feedback decreases, as the mean block width increases. The ARSB method shows superior performance, accurately detecting both self-feedback and causality across all variables. Regarding the analysis of the Impulse Response Function (IRF), only the ARSB method succeeds in detecting both self-feedback and causality in all variables, aligning well with the connectivity diagram. Other methods, however, show considerable variations in detection performance, with some detecting false positives and others only detecting self-feedback.
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Affiliation(s)
- Fumikazu Miwakeichi
- Department of Statistical Modeling, The Institute of Statistical Mathematics, Tokyo 190-8562, Japan
- Statistical Science Program, Graduate Institute for Advanced Studies, SOKENDAI, Tokyo 190-8562, Japan
| | - Andreas Galka
- Clinic for Pediatric and Adolescent Medicine II, University Clinic, University of Kiel, 24105 Kiel, Germany
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8
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Bahamonde AD, Montes RM, Cornejo P. Usefulness and limitations of convergent cross sorting and continuity scaling methods for their application in simulated and real-world time series. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221590. [PMID: 37448474 PMCID: PMC10336384 DOI: 10.1098/rsos.221590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/21/2023] [Indexed: 07/15/2023]
Abstract
Causality detection methods are valuable tools for detecting causal links in complex systems. The efficiency of continuity scaling (CS) and the convergent cross sorting (CSS) methods to detect causality was analysed. Usefulness and limitations of both methods in their application to simulated and real-world time series was explored under different scenarios. We find that CS is more robust and efficient than the CSS method for all simulated systems, even when increasing noise levels were considered. Both methods were not able to infer causality when time series with a marked difference in their main frequencies were analysed. Minimum time-series length required for the detection of a causal link depends on intrinsic system dynamics and on the method selected to detect it. Using simulated time series, only the CS method was capable to detect bidirectional causality. Causality detection, using the CS method, should at least include: (i) causality strength convergence analysis, (ii) statistical tests of significance, (iii) time-series standardization, and (iv) causality strength ratios as a strength indicator of relative causality between systems. Causality cannot be detected by either method in simulated time series that exhibit generalized synchronization.
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Affiliation(s)
- Adolfo D Bahamonde
- Interdisciplinary Center for Aquaculture Research (INCAR), University of Concepción, O'Higgins 1695, Concepción, Chile
| | - Rodrigo M Montes
- Interdisciplinary Center for Aquaculture Research (INCAR), University of Concepción, O'Higgins 1695, Concepción, Chile
| | - Pablo Cornejo
- Interdisciplinary Center for Aquaculture Research (INCAR), University of Concepción, O'Higgins 1695, Concepción, Chile
- Mechanical Engineering Department, University of Concepción, Concepción, Chile
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9
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Glaba P, Latka M, Krause MJ, Kroczka S, Kuryło M, Kaczorowska-Frontczak M, Walas W, Jernajczyk W, Sebzda T, West BJ. EEG phase synchronization during absence seizures. Front Neuroinform 2023; 17:1169584. [PMID: 37404335 PMCID: PMC10317177 DOI: 10.3389/fninf.2023.1169584] [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: 02/19/2023] [Accepted: 05/25/2023] [Indexed: 07/06/2023] Open
Abstract
Absence seizures-generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies. Such seizures are the most compelling examples of pathological neuronal hypersynchrony. All the absence detection algorithms proposed so far have been derived from the properties of individual SWDs. In this work, we investigate EEG phase synchronization in patients with CAE/JAE and healthy subjects to explore the possibility of using the wavelet phase synchronization index to detect seizures and quantify their disorganization (fragmentation). The overlap of the ictal and interictal probability density functions was high enough to preclude effective seizure detection based solely on changes in EEG synchronization. We used a machine learning classifier with the phase synchronization index (calculated for 1 s data segments with 0.5 s overlap) and the normalized amplitude as features to detect generalized SWDs. Using 19 channels (10-20 setup), we identified 99.2% of absences. However, the overlap of the segments classified as ictal with seizures was only 83%. The analysis showed that seizures were disorganized in approximately half of the 65 subjects. On average, generalized SWDs lasted about 80% of the duration of abnormal EEG activity. The disruption of the ictal rhythm can manifest itself as the disappearance of epileptic spikes (with high-amplitude delta waves persisting), transient cessation of epileptic discharges, or loss of global synchronization. The detector can analyze a real-time data stream. Its performance is good for a six-channel setup (Fp1, Fp2, F7, F8, O1, O2), which can be implemented as an unobtrusive EEG headband. False detections are rare for controls and young adults (0.03% and 0.02%, respectively). In patients, they are more frequent (0.5%), but in approximately 82% cases, classification errors are caused by short epileptiform discharges. Most importantly, the proposed detector can be applied to parts of EEG with abnormal EEG activity to quantitatively determine seizure fragmentation. This property is important because a previous study reported that the probability of disorganized discharges is eight times higher in JAE than in CAE. Future research must establish whether seizure properties (frequency, length, fragmentation, etc.) and clinical characteristics can help distinguish CAE and JAE.
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Affiliation(s)
- Pawel Glaba
- Department of Biomedical Engineering, Wroclaw University of Science and Technology, Wrocław, Poland
| | - Miroslaw Latka
- Department of Biomedical Engineering, Wroclaw University of Science and Technology, Wrocław, Poland
| | | | - Sławomir Kroczka
- Department of Child Neurology, Jagiellonian University Medical College, Kraków, Poland
| | - Marta Kuryło
- Department of Pediatric Neurology, T. Marciniak Hospital, Wrocław, Poland
| | | | - Wojciech Walas
- Department of Anesthesiology, Intensive Care and Regional Extracorporeal Membrane Oxygenation (ECMO) Center, Institute of Medical Sciences, University of Opole, Opole, Poland
| | - Wojciech Jernajczyk
- Clinical Neurophysiology, Institute of Psychiatry and Neurology, Warszawa, Poland
| | - Tadeusz Sebzda
- Department of Physiology and Pathophysiology, Medical University of Wroclaw, Wrocław, Poland
| | - Bruce J. West
- Center for Nonlinear Science, University of North Texas, Denton, TX, United States
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Zhuravlev M, Agaltsov M, Kiselev A, Simonyan M, Novikov M, Selskii A, Ukolov R, Drapkina O, Orlova A, Penzel T, Runnova A. Compensatory mechanisms of reduced interhemispheric EEG connectivity during sleep in patients with apnea. Sci Rep 2023; 13:8444. [PMID: 37231107 PMCID: PMC10213009 DOI: 10.1038/s41598-023-35376-1] [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: 10/01/2022] [Accepted: 05/17/2023] [Indexed: 05/27/2023] Open
Abstract
We performed a mathematical analysis of functional connectivity in electroencephalography (EEG) of patients with obstructive sleep apnea (OSA) (N = 10; age: 52.8 ± 13 years; median age: 49 years; male/female ratio: 7/3), compared with a group of apparently healthy participants (N = 15; age: 51.5 ± 29.5 years; median age: 42 years; male/female ratio: 8/7), based on the calculation of wavelet bicoherence from nighttime polysomnograms. Having observed the previously known phenomenon of interhemispheric synchronization deterioration, we demonstrated a compensatory increase in intrahemispheric connectivity, as well as a slight increase in the connectivity of the central and occipital areas for high-frequency EEG activity. Significant changes in functional connectivity were extremely stable in groups of apparently healthy participants and OSA patients, maintaining the overall pattern when comparing different recording nights and various sleep stages. The maximum variability of the connectivity was observed at fast oscillatory processes during REM sleep. The possibility of observing some changes in functional connectivity of brain activity in OSA patients in a state of passive wakefulness opens up prospects for further research. Developing the methods of hypnogram evaluation that are independent of functional connectivity may be useful for implementing a medical decision support system.
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Affiliation(s)
- Maksim Zhuravlev
- National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia
- Institute of Physics, Saratov State University, Saratov, Russia
| | - Mikhail Agaltsov
- National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia
| | - Anton Kiselev
- National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia
| | - Margarita Simonyan
- Institute of Physics, Saratov State University, Saratov, Russia
- Institute of Cardiology Research, Saratov State Medical University, Saratov, Russia
| | - Mikhail Novikov
- Institute of Cardiology Research, Saratov State Medical University, Saratov, Russia
| | - Anton Selskii
- Institute of Physics, Saratov State University, Saratov, Russia
| | - Rodion Ukolov
- Institute of Physics, Saratov State University, Saratov, Russia
| | - Oksana Drapkina
- National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia
| | - Anna Orlova
- National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia
| | - Thomas Penzel
- Interdisciplinary Sleep Medicine Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Anastasiya Runnova
- National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia.
- Institute of Physics, Saratov State University, Saratov, Russia.
- Institute of Cardiology Research, Saratov State Medical University, Saratov, Russia.
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Ji J, Zou A, Liu J, Yang C, Zhang X, Song Y. A Survey on Brain Effective Connectivity Network Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:1879-1899. [PMID: 34469315 DOI: 10.1109/tnnls.2021.3106299] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of the pathological mechanism associated with neuropsychiatric diseases and facilitate finding new brain network imaging markers for the early diagnosis and evaluation for the treatment of cerebral diseases. A deeper understanding of brain ECNs also greatly promotes brain-inspired artificial intelligence (AI) research in the context of brain-like neural networks and machine learning. Thus, how to picture and grasp deeper features of brain ECNs from functional magnetic resonance imaging (fMRI) data is currently an important and active research area of the human brain connectome. In this survey, we first show some typical applications and analyze existing challenging problems in learning brain ECNs from fMRI data. Second, we give a taxonomy of ECN learning methods from the perspective of computational science and describe some representative methods in each category. Third, we summarize commonly used evaluation metrics and conduct a performance comparison of several typical algorithms both on simulated and real datasets. Finally, we present the prospects and references for researchers engaged in learning ECNs.
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12
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Hancock F, Rosas FE, McCutcheon RA, Cabral J, Dipasquale O, Turkheimer FE. Metastability as a candidate neuromechanistic biomarker of schizophrenia pathology. PLoS One 2023; 18:e0282707. [PMID: 36952467 PMCID: PMC10035891 DOI: 10.1371/journal.pone.0282707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/21/2023] [Indexed: 03/25/2023] Open
Abstract
The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain, and is therefore well suited for the study of schizophrenia. In this study, we investigate metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, pathophysiological relevance, and explanatory power were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n = 53, non-affective psychosis n = 82) and the Cobre study (controls n = 71, cases n = 59). In this work we extend Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implement a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naïve Bayes classifier to discriminate cases from controls. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. Additionally, our new metric showed explanatory power of between 81-92% for measures of integration and segregation. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Overall, these findings demonstrate reliability and face validity of metastability as a candidate neuromechanistic biomarker of schizophrenia pathology.
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Affiliation(s)
- Fran Hancock
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
| | - Fernando E. Rosas
- Department of Informatics, University of Sussex, Brighton, United Kingdom
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
| | - Robert A. McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Life and Health Sciences Research Institute School of Medicine, University of Minho, Braga, Portugal
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
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13
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Barà C, Sparacino L, Pernice R, Antonacci Y, Porta A, Kugiumtzis D, Faes L. Comparison of discretization strategies for the model-free information-theoretic assessment of short-term physiological interactions. CHAOS (WOODBURY, N.Y.) 2023; 33:033127. [PMID: 37003789 DOI: 10.1063/5.0140641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/17/2023] [Indexed: 06/19/2023]
Abstract
This work presents a comparison between different approaches for the model-free estimation of information-theoretic measures of the dynamic coupling between short realizations of random processes. The measures considered are the mutual information rate (MIR) between two random processes X and Y and the terms of its decomposition evidencing either the individual entropy rates of X and Y and their joint entropy rate, or the transfer entropies from X to Y and from Y to X and the instantaneous information shared by X and Y. All measures are estimated through discretization of the random variables forming the processes, performed either via uniform quantization (binning approach) or rank ordering (permutation approach). The binning and permutation approaches are compared on simulations of two coupled non-identical Hènon systems and on three datasets, including short realizations of cardiorespiratory (CR, heart period and respiration flow), cardiovascular (CV, heart period and systolic arterial pressure), and cerebrovascular (CB, mean arterial pressure and cerebral blood flow velocity) measured in different physiological conditions, i.e., spontaneous vs paced breathing or supine vs upright positions. Our results show that, with careful selection of the estimation parameters (i.e., the embedding dimension and the number of quantization levels for the binning approach), meaningful patterns of the MIR and of its components can be achieved in the analyzed systems. On physiological time series, we found that paced breathing at slow breathing rates induces less complex and more coupled CR dynamics, while postural stress leads to unbalancing of CV interactions with prevalent baroreflex coupling and to less complex pressure dynamics with preserved CB interactions. These results are better highlighted by the permutation approach, thanks to its more parsimonious representation of the discretized dynamic patterns, which allows one to explore interactions with longer memory while limiting the curse of dimensionality.
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Affiliation(s)
- Chiara Barà
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Yuri Antonacci
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Alberto Porta
- Department of Biomedical Sciences for Health, University of Milan, 20133 Milan, Italy
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
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14
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Fotiadis A, Vlachos I, Kugiumtzis D. Detecting Nonlinear Interactions in Complex Systems: Application in Financial Markets. ENTROPY (BASEL, SWITZERLAND) 2023; 25:370. [PMID: 36832737 PMCID: PMC9954853 DOI: 10.3390/e25020370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Emerging or diminishing nonlinear interactions in the evolution of a complex system may signal a possible structural change in its underlying mechanism. This type of structural break may exist in many applications, such as in climate and finance, and standard methods for change-point detection may not be sensitive to it. In this article, we present a novel scheme for detecting structural breaks through the occurrence or vanishing of nonlinear causal relationships in a complex system. A significance resampling test was developed for the null hypothesis (H0) of no nonlinear causal relationships using (a) an appropriate Gaussian instantaneous transform and vector autoregressive (VAR) process to generate the resampled multivariate time series consistent with H0; (b) the modelfree Granger causality measure of partial mutual information from mixed embedding (PMIME) to estimate all causal relationships; and (c) a characteristic of the network formed by PMIME as test statistic. The significance test was applied to sliding windows on the observed multivariate time series, and the change from rejection to no-rejection of H0, or the opposite, signaled a non-trivial change of the underlying dynamics of the observed complex system. Different network indices that capture different characteristics of the PMIME networks were used as test statistics. The test was evaluated on multiple synthetic complex and chaotic systems, as well as on linear and nonlinear stochastic systems, demonstrating that the proposed methodology is capable of detecting nonlinear causality. Furthermore, the scheme was applied to different records of financial indices regarding the global financial crisis of 2008, the two commodity crises of 2014 and 2020, the Brexit referendum of 2016, and the outbreak of COVID-19, accurately identifying the structural breaks at the identified times.
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Affiliation(s)
- Akylas Fotiadis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Ioannis Vlachos
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
- 1st Department of Neurology, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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15
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Yin J, Wang Y. Topological inference and correlation of signals with application to electroencephalography in epilepsy. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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16
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Xu F, Zhao J, Liu M, Yu X, Wang C, Lou Y, Shi W, Liu Y, Gao L, Yang Q, Zhang B, Lu S, Tang J, Leng J. Exploration of sleep function connection and classification strategies based on sub-period sleep stages. Front Neurosci 2023; 16:1088116. [PMID: 36760796 PMCID: PMC9906994 DOI: 10.3389/fnins.2022.1088116] [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: 11/03/2022] [Accepted: 12/30/2022] [Indexed: 01/26/2023] Open
Abstract
Background As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. Methods Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages. Results The experimental results have shown that when the number of sub-periods is 30, the α (8-13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%. Conclusion The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.
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Affiliation(s)
- Fangzhou Xu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China,*Correspondence: Fangzhou Xu,
| | - Jinzhao Zhao
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Ming Liu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Xin Yu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Chongfeng Wang
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Yitai Lou
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Weiyou Shi
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Yanbing Liu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Licai Gao
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Qingbo Yang
- School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Baokun Zhang
- Department of Neurology, Shandong Institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Shanshan Lu
- Department of Neurology, Shandong Institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China,Department of Neurology, Cheeloo College of Medicine, Shandong Qianfoshan Hospital, Shandong University, Jinan, Shandong, China,Shanshan Lu,
| | - Jiyou Tang
- Department of Neurology, Shandong Institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, The First Affliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China,Department of Neurology, Cheeloo College of Medicine, Shandong Qianfoshan Hospital, Shandong University, Jinan, Shandong, China,Jiyou Tang,
| | - Jiancai Leng
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China,Jiancai Leng,
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17
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Jörg DJ, Fuertinger DH, Kotanko P. Mechanisms of hemoglobin cycling in anemia patients treated with erythropoiesis-stimulating agents. PLoS Comput Biol 2023; 19:e1010850. [PMID: 36693034 PMCID: PMC9873166 DOI: 10.1371/journal.pcbi.1010850] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 01/04/2023] [Indexed: 01/25/2023] Open
Abstract
Patients with renal anemia are frequently treated with erythropoiesis-stimulating agents (ESAs), which are dynamically dosed in order to stabilize blood hemoglobin levels within a specified target range. During typical ESA treatments, a fraction of patients experience hemoglobin 'cycling' periods during which hemoglobin levels periodically over- and undershoot the target range. Here we report a specific mechanism of hemoglobin cycling, whereby cycles emerge from the patient's delayed physiological response to ESAs and concurrent ESA dose adjustments. We introduce a minimal theoretical model that can explain dynamic hallmarks of observed hemoglobin cycling events in clinical time series and elucidates how physiological factors (such as red blood cell lifespan and ESA responsiveness) and treatment-related factors (such as dosing schemes) affect cycling. These results show that in general, hemoglobin cycling cannot be attributed to patient physiology or ESA treatment alone but emerges through an interplay of both, with consequences for the design of ESA treatment strategies.
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Affiliation(s)
- David J. Jörg
- Computational Medicine Group, Global Medical Office, Fresenius Medical Care Germany, Bad Homburg, Germany
- * E-mail:
| | - Doris H. Fuertinger
- Computational Medicine Group, Global Medical Office, Fresenius Medical Care Germany, Bad Homburg, Germany
| | - Peter Kotanko
- Renal Research Institute, New York, New York, United States of America
- Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
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18
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Papapetrou M, Siggiridou E, Kugiumtzis D. Adaptation of Partial Mutual Information from Mixed Embedding to Discrete-Valued Time Series. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1505. [PMID: 36359599 PMCID: PMC9689532 DOI: 10.3390/e24111505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/14/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
A causality analysis aims at estimating the interactions of the observed variables and subsequently the connectivity structure of the observed dynamical system or stochastic process. The partial mutual information from mixed embedding (PMIME) is found appropriate for the causality analysis of continuous-valued time series, even of high dimension, as it applies a dimension reduction by selecting the most relevant lag variables of all the observed variables to the response, using conditional mutual information (CMI). The presence of lag components of the driving variable in this vector implies a direct causal (driving-response) effect. In this study, the PMIME is appropriately adapted to discrete-valued multivariate time series, called the discrete PMIME (DPMIME). An appropriate estimation of the discrete probability distributions and CMI for discrete variables is implemented in the DPMIME. Further, the asymptotic distribution of the estimated CMI is derived, allowing for a parametric significance test for the CMI in the DPMIME, whereas for the PMIME, there is no parametric test for the CMI and the test is performed using resampling. Monte Carlo simulations are performed using different generating systems of discrete-valued time series. The simulation suggests that the parametric significance test for the CMI in the progressive algorithm of the DPMIME is compared favorably to the corresponding resampling significance test, and the accuracy of the DPMIME in the estimation of direct causality converges with the time-series length to the accuracy of the PMIME. Further, the DPMIME is used to investigate whether the global financial crisis has an effect on the causality network of the financial world market.
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19
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Kobayashi S, O'Hashi K, Kobayashi M. Repetitive nociceptive stimulation increases spontaneous neural activation similar to nociception-induced activity in mouse insular cortex. Sci Rep 2022; 12:15190. [PMID: 36071208 PMCID: PMC9452502 DOI: 10.1038/s41598-022-19562-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 08/31/2022] [Indexed: 11/09/2022] Open
Abstract
Recent noninvasive neuroimaging technology has revealed that spatiotemporal patterns of cortical spontaneous activity observed in chronic pain patients are different from those in healthy subjects, suggesting that the spontaneous cortical activity plays a key role in the induction and/or maintenance of chronic pain. However, the mechanisms of the spontaneously emerging activities supposed to be induced by nociceptive inputs remain to be established. In the present study, we investigated spontaneous cortical activities in sessions before and after electrical stimulation of the periodontal ligament (PDL) by applying wide-field and two-photon calcium imaging to anesthetized GCaMP6s transgenic mice. First, we identified the sequential cortical activation patterns from the primary somatosensory and secondary somatosensory cortices to the insular cortex (IC) by PDL stimulation. We, then found that spontaneous IC activities that exhibited a similar spatiotemporal cortical pattern to evoked activities by PDL stimulation increased in the session after repetitive PDL stimulation. At the single-cell level, repetitive PDL stimulation augmented the synchronous neuronal activity. These results suggest that cortical plasticity induced by the repetitive stimulation leads to the frequent PDL stimulation-evoked-like spontaneous IC activation. This nociception-induced spontaneous activity in IC may be a part of mechanisms that induces chronic pain.
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Affiliation(s)
- Shutaro Kobayashi
- Department of Pharmacology, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan.,Department of Oral Surgery, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan
| | - Kazunori O'Hashi
- Department of Pharmacology, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan. .,Division of Oral and Craniomaxillofacial Research, Dental Research Center, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan. .,Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, 187-8502, Japan.
| | - Masayuki Kobayashi
- Department of Pharmacology, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan. .,Division of Oral and Craniomaxillofacial Research, Dental Research Center, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan. .,Molecular Imaging Research Center, RIKEN, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe, 650-0047, Japan.
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20
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Wang L, Qiao M, Tao H, Song X, Shao Q, Wang C, Yang H, Niu W, Chen Y. A comparison of muscle activation and concomitant intermuscular coupling of antagonist muscles among bench presses with different instability degrees in untrained men. Front Physiol 2022; 13:940719. [PMID: 36148298 PMCID: PMC9486837 DOI: 10.3389/fphys.2022.940719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 07/15/2022] [Indexed: 11/30/2022] Open
Abstract
The aim of this study was to analyze and compare the muscle activation and concomitant intermuscular coupling of antagonist muscles among bench presses with different instability degrees. Twenty-nine untrained male college students performed bench press exercises at an intensity of 60% 1 RM on three conditions: small unstable bench press with Smith machine (SBP), medium unstable bench press of free weight (FWBP), and large unstable bench press with increased instability by suspending the load with elastic bands (IIBP). One-way repeated measures analysis of variance was used to compare integrated EMG activity values of the biceps brachii (BB), posterior deltoid (PD), long head of the triceps brachii (TB), anterior deltoid (AD), upper portion of the pectoralis major (PM) muscles, and phase synchronization index (PSI) of BB-TB and PD-AD antagonist muscle pairs. A higher integrated EMG of BB muscle was found during bench press with a more unstable condition. IIBP showed a higher integrated EMG of prime movers (TB, AD, and PM) and stabilizing of BB than SBP and FWBP. PSI between muscle pairs of BB-TB in the gamma frequency band was higher in SBP than the other bench presses with unstable conditions, which may be related to the optimal “internal model” for antagonist muscles during bench press exercise. Therefore, IIBP training may be an effective accessory exercise to maintain a higher level of muscle activation across primary and stabilizing muscles with a lighter load for untrained men, while SBP may be a suitable bench press exercise for untrained participants who have not developed the neuromuscular adaptations necessary for correct stabilization of the elbow joint.
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Affiliation(s)
- Lejun Wang
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
- *Correspondence: Lejun Wang, ; Yiqing Chen,
| | - Minjie Qiao
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Haifeng Tao
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Xiaoqian Song
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Qineng Shao
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
- Engineering Research Center of Clinical Translational Digital Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ce Wang
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Hua Yang
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Wenxin Niu
- School of Medicine, Tongji University, Shanghai, China
| | - Yiqing Chen
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
- *Correspondence: Lejun Wang, ; Yiqing Chen,
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21
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Wang Z, Wong CM, Nan W, Tang Q, Rosa AC, Xu P, Wan F. Learning Curve of a Short-Time Neurofeedback Training: Reflection of Brain Network Dynamics Based on Phase-Locking Value. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2021.3125948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Ze Wang
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, Centre for Cognitive and Brain Sciences, and the Centre for Artificial Intelligence and Robotics, Institute of Collaborative Innovation, University of Macau, Macau, China
| | - Chi Man Wong
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, Centre for Cognitive and Brain Sciences, and the Centre for Artificial Intelligence and Robotics, Institute of Collaborative Innovation, University of Macau, Macau, China
| | - Wenya Nan
- Department of Psychology, Shanghai Normal University, Shanghai, China
| | - Qi Tang
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, Centre for Cognitive and Brain Sciences, and the Centre for Artificial Intelligence and Robotics, Institute of Collaborative Innovation, University of Macau, Macau, China
| | - Agostinho C. Rosa
- Department of Bioengineering, LaSEEBSystem and Robotics Institute, Instituto Superior Tecnico, University of Lisbon, Lisbon, Portugal
| | - Peng Xu
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng Wan
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, Centre for Cognitive and Brain Sciences, and the Centre for Artificial Intelligence and Robotics, Institute of Collaborative Innovation, University of Macau, Macau, China
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22
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Panda R, Thibaut A, Lopez-Gonzalez A, Escrichs A, Bahri MA, Hillebrand A, Deco G, Laureys S, Gosseries O, Annen J, Tewarie P. Disruption in structural-functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness. eLife 2022; 11:77462. [PMID: 35916363 PMCID: PMC9385205 DOI: 10.7554/elife.77462] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace (GNW) and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, the prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, that is so-called eigenmodes, was supplemented as potential markers. By analysing time-resolved functional connectivity from functional MRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and subcortical fronto-temporoparietal network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the GNW theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.
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Affiliation(s)
| | | | - Ane Lopez-Gonzalez
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gustavo Deco
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | | | | | - Jitka Annen
- Coma Science Group, University of Liège, Liège, Belgium
| | - Prejaas Tewarie
- School of Physics, University of Nottingham, Nottingham, United Kingdom
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23
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Manoj K, Pawar SA, Kurths J, Sujith RI. Rijke tube: A nonlinear oscillator. CHAOS (WOODBURY, N.Y.) 2022; 32:072101. [PMID: 35907738 DOI: 10.1063/5.0091826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/16/2022] [Indexed: 06/15/2023]
Abstract
Dynamical systems theory has emerged as an interdisciplinary area of research to characterize the complex dynamical transitions in real-world systems. Various nonlinear dynamical phenomena and bifurcations have been discovered over the decades using different reduced-order models of oscillators. Different measures and methodologies have been developed theoretically to detect, control, or suppress the nonlinear oscillations. However, obtaining such phenomena experimentally is often challenging, time-consuming, and risky mainly due to the limited control of certain parameters during experiments. With this review, we aim to introduce a paradigmatic and easily configurable Rijke tube oscillator to the dynamical systems community. The Rijke tube is commonly used by the combustion community as a prototype to investigate the detrimental phenomena of thermoacoustic instability. Recent investigations in such Rijke tubes have utilized various methodologies from dynamical systems theory to better understand the occurrence of thermoacoustic oscillations and their prediction and mitigation, both experimentally and theoretically. The existence of various dynamical behaviors has been reported in single and coupled Rijke tube oscillators. These behaviors include bifurcations, routes to chaos, noise-induced transitions, synchronization, and suppression of oscillations. Various early warning measures have been established to predict thermoacoustic instabilities. Therefore, this review article consolidates the usefulness of a Rijke tube oscillator in terms of experimentally discovering and modeling different nonlinear phenomena observed in physics, thus transcending the boundaries between the physics and the engineering communities.
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Affiliation(s)
- Krishna Manoj
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Samadhan A Pawar
- Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai 600036, India
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, Potsdam 14473, Germany
| | - R I Sujith
- Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai 600036, India
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24
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Wang X, Sipahi R, Porfiri M. Spatiotemporal patterns of firearm acquisition in the United States in different presidential terms. CHAOS (WOODBURY, N.Y.) 2022; 32:073115. [PMID: 35907731 DOI: 10.1063/5.0096773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
This study develops mathematical tools and approaches to investigate spatiotemporal patterns of firearm acquisition in the U.S. complemented by hypothesis testing and statistical analysis. First, state-level and nation-level instant background check (BC) data are employed as proxy of firearm acquisition corresponding to 1999-2021. The relative-phase time-series of BC in each U.S. state is recovered and utilized to calculate the time-series of the U.S. states' synchronization degree. We reveal that U.S. states present a high-level degree of synchronization except in 2010-2011 and after 2018. Comparing these results with respect to a sitting U.S. president provides additional information: specifically, any two presidential terms are characterized by statistically different synchronization degrees except G. W. Bush's first term and B. H. Obama's second term. Next, to detail variations of BC, short-time Fourier transform, dimensionality reduction techniques, and diffusion maps are implemented within a time-frequency representation. Firearm acquisition in the high frequency band is described by a low-dimensional embedding, in the form of a plane with two embedding coordinates. Data points on the embedding plane identify separate clusters that signify state transitions in the original BC data with respect to different time windows. Through this analysis, we reveal that the frequency content of the BC data has a time-dependent characteristic. By comparing the diffusion map at hand with respect to a presidential term, we find that at least one of the embedding coordinates presents statistically significant variations between any two presidential terms except B. H. Obama's first term and D. J. Trump's pre-COVID term. The results point at a possible interplay between firearm acquisition in the U.S. and a presidential term.
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Affiliation(s)
- Xu Wang
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA
| | - Rifat Sipahi
- Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA
| | - Maurizio Porfiri
- Center for Urban Science and Progress, Tandon School of Engineering, New York University, Brooklyn, New York 11201, USA
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25
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Hancock F, Cabral J, Luppi AI, Rosas FE, Mediano PAM, Dipasquale O, Turkheimer FE. Metastability, fractal scaling, and synergistic information processing: what phase relationships reveal about intrinsic brain activity. Neuroimage 2022; 259:119433. [PMID: 35781077 PMCID: PMC9339663 DOI: 10.1016/j.neuroimage.2022.119433] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 06/25/2022] [Accepted: 06/29/2022] [Indexed: 12/21/2022] Open
Abstract
Spatiotemporal patterns of phase-locking tend to be time-invariant. Global metastability is representative and stable in a cohort of heathy young adults. dFC characteristics are in general unique to any fMRI acquisition. Dynamical and informational complexity are interrelated. Complexity science contributes to a coherent description of brain dynamics.
Dynamic functional connectivity (dFC) in resting-state fMRI holds promise to deliver candidate biomarkers for clinical applications. However, the reliability and interpretability of dFC metrics remain contested. Despite a myriad of methodologies and resulting measures, few studies have combined metrics derived from different conceptualizations of brain functioning within the same analysis - perhaps missing an opportunity for improved interpretability. Using a complexity-science approach, we assessed the reliability and interrelationships of a battery of phase-based dFC metrics including tools originating from dynamical systems, stochastic processes, and information dynamics approaches. Our analysis revealed novel relationships between these metrics, which allowed us to build a predictive model for integrated information using metrics from dynamical systems and information theory. Furthermore, global metastability - a metric reflecting simultaneous tendencies for coupling and decoupling - was found to be the most representative and stable metric in brain parcellations that included cerebellar regions. Additionally, spatiotemporal patterns of phase-locking were found to change in a slow, non-random, continuous manner over time. Taken together, our findings show that the majority of characteristics of resting-state fMRI dynamics reflect an interrelated dynamical and informational complexity profile, which is unique to each acquisition. This finding challenges the interpretation of results from cross-sectional designs for brain neuromarker discovery, suggesting that individual life-trajectories may be more informative than sample means.
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Affiliation(s)
- Fran Hancock
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Portugal
| | - Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge; Department of Clinical Neurosciences, University of Cambridge; Leverhulme Centre for the Future of Intelligence, University of Cambridge; Alan Turing Institute, London, UK
| | - Fernando E Rosas
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London SW7 2DD; Data Science Institute, Imperial College London, London SW7 2AZ; Centre for Complexity Science, Imperial College London, London SW7 2AZ
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB; Department of Psychology, Queen Mary University of London, London E1 4NS
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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26
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Hancock F, Rosas FE, Mediano PAM, Luppi AI, Cabral J, Dipasquale O, Turkheimer FE. May the 4C's be with you: an overview of complexity-inspired frameworks for analysing resting-state neuroimaging data. J R Soc Interface 2022; 19:20220214. [PMID: 35765805 PMCID: PMC9240685 DOI: 10.1098/rsif.2022.0214] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/09/2022] [Indexed: 11/12/2022] Open
Abstract
Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behaviour. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here, we review and clarify the key concepts of connectivity, computation, criticality and coherence-the 4C's-and outline a potential role for metastability as a common denominator across these propositions. We analyse and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging, to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.
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Affiliation(s)
- Fran Hancock
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Fernando E. Rosas
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London SW7 2DD, UK
- Data Science Institute, Imperial College London, London SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London SW7 2AZ, UK
| | - Pedro A. M. Mediano
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
- Department of Psychology, Queen Mary University of London, London E1 4NS, UK
| | - Andrea I. Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK
- Alan Turing Institute, London, UK
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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27
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Kaminski M, Blinowska KJ. From Coherence to Multivariate Causal Estimators of EEG Connectivity. Front Physiol 2022; 13:868294. [PMID: 35557965 PMCID: PMC9086354 DOI: 10.3389/fphys.2022.868294] [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: 02/02/2022] [Accepted: 03/15/2022] [Indexed: 11/17/2022] Open
Abstract
The paper concerns the development of methods of EEG functional connectivity estimation including short overview of the currently applied measures describing their advantages and flaws. Linear and non-linear, bivariate and multivariate methods are confronted. The performance of different connectivity measures in respect of robustness to noise, common drive effect and volume conduction is considered providing a guidance towards future developments in the field, which involve evaluation not only functional, but also effective (causal) connectivity. The time-varying connectivity measure making possible estimation of dynamical information processing in brain is presented. The methods of post-processing of connectivity results are considered involving application of advanced graph analysis taking into account community structure of networks and providing hierarchy of networks rather than the single, binary networks currently used.
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Affiliation(s)
- Maciej Kaminski
- Department of Biomedical Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland
| | - Katarzyna J Blinowska
- Department of Biomedical Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland.,Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Poland
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28
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Espinoso A, Andrzejak RG. Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients. Phys Rev E 2022; 105:034212. [PMID: 35428047 DOI: 10.1103/physreve.105.034212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
The severe neurological disorder epilepsy affects almost 1% of the world population. For patients who suffer from pharmacoresistant focal-onset epilepsy, electroencephalographic (EEG) recordings are essential for the localization of the brain area where seizures start. Apart from the visual inspection of the recordings, quantitative EEG signal analysis techniques proved to be useful for this purpose. Among other features, regularity versus irregularity and phase coherence versus phase independence allowed characterizing brain dynamics from the measured EEG signals. Can phase irregularities also characterize brain dynamics? To address this question, we use the univariate coefficient of phase velocity variation, defined as the ratio of phase velocity standard deviation and the mean phase velocity. Beyond that, as a bivariate measure we use the classical mean phase coherence to quantify the degree of phase locking. All phase-based measures are combined with surrogates to test null hypotheses about the dynamics underlying the signals. In the first part of our analysis, we use the Rössler model system to study our approach under controlled conditions. In the second part, we use the Bern-Barcelona EEG database which consists of focal and nonfocal signals extracted from seizure-free recordings. Focal signals are recorded from brain areas where the first seizure EEG signal changes can be detected, and nonfocal signals are recorded from areas that are not involved in the seizure at its onset. Our results show that focal signals have less phase variability and more phase coherence than nonfocal signals. Once combined with surrogates, the mean phase velocity proved to have the highest discriminative power between focal and nonfocal signals. In conclusion, conceptually simple and easy to compute phase-based measures can help to detect features induced by epilepsy from EEG signals. This holds not only for the classical mean phase coherence but even more so for univariate measures of phase irregularity.
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Affiliation(s)
- Anaïs Espinoso
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain and Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Carrer Baldiri Reixac 10-12, 08028 Barcelona, Catalonia, Spain
| | - Ralph G Andrzejak
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Carrer Roc Boronat 138, 08018 Barcelona, Catalonia, Spain
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29
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Wang L, Wang C, Yang H, Shao Q, Niu W, Yang Y, Zheng F. Halo Sport Transcranial Direct Current Stimulation Improved Muscular Endurance Performance and Neuromuscular Efficiency During an Isometric Submaximal Fatiguing Elbow Flexion Task. Front Hum Neurosci 2022; 16:758891. [PMID: 35250511 PMCID: PMC8891483 DOI: 10.3389/fnhum.2022.758891] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 01/20/2022] [Indexed: 12/21/2022] Open
Abstract
The present study examined the effects of transcranial direct current stimulation (tDCS) using Halo Sport on the time to exhaustion (TTE) in relation with muscle activities and corticomuscular coupling of agonist and antagonist muscles during a sustained isometric fatiguing contraction performed with the elbow flexors. Twenty healthy male college students were randomly assigned to tDCS group and control group. The two group participants performed two experimental sessions which consisted of pre-fatigue isometric maximal voluntary contraction (MVC), sustained submaximal voluntary contractions (30% maximal torque) performed to exhaustion, and post-fatigue MVC with the right elbow flexor muscles. Sham stimulation (90 s) and tDCS (20 min) were applied for control and tDCS group participants 20 min prior to the second session test, respectively. MVC strength in pre- and post-fatigue test, TTE, electroencephalogram (EEG), and electromyography (EMG) of biceps brachii (BB) and triceps brachii (TB) were recorded during the tests. It was found that tDCS using the Halo Sport device significantly increased TTE and thus improved muscular endurance performance. The improvement may be partly related to the improvement of neuromuscular efficiency as reflected by decrease of antagonistic muscle coactivation activities, which may be related to cortical originated central processing mechanism of neuromuscular activities.
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Affiliation(s)
- Lejun Wang
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Ce Wang
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Hua Yang
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Qineng Shao
- Sport and Health Research Center, Physical Education Department, Tongji University, Shanghai, China
| | - Wenxin Niu
- Shanghai Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ye Yang
- College of Physical Education and Health Science, Yibin University, Sichuan, China
- *Correspondence: Ye Yang,
| | - Fanhui Zheng
- Shanghai Research Institute of Sport Science, Shanghai, China
- Fanhui Zheng,
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30
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Monitoring the Evolution of Asynchrony between Mean Arterial Pressure and Mean Cerebral Blood Flow via Cross-Entropy Methods. ENTROPY 2022; 24:e24010080. [PMID: 35052106 PMCID: PMC8774596 DOI: 10.3390/e24010080] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/29/2021] [Accepted: 12/30/2021] [Indexed: 02/05/2023]
Abstract
Cerebrovascular control is carried out by multiple nonlinear mechanisms imposing a certain degree of coupling between mean arterial pressure (MAP) and mean cerebral blood flow (MCBF). We explored the ability of two nonlinear tools in the information domain, namely cross-approximate entropy (CApEn) and cross-sample entropy (CSampEn), to assess the degree of asynchrony between the spontaneous fluctuations of MAP and MCBF. CApEn and CSampEn were computed as a function of the translation time. The analysis was carried out in 23 subjects undergoing recordings at rest in supine position (REST) and during active standing (STAND), before and after surgical aortic valve replacement (SAVR). We found that at REST the degree of asynchrony raised, and the rate of increase in asynchrony with the translation time decreased after SAVR. These results are likely the consequence of the limited variability of MAP observed after surgery at REST, more than the consequence of a modified cerebrovascular control, given that the observed differences disappeared during STAND. CApEn and CSampEn can be utilized fruitfully in the context of the evaluation of cerebrovascular control via the noninvasive acquisition of the spontaneous MAP and MCBF variability.
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31
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Borovkova EI, Prokhorov MD, Kiselev AR, Hramkov AN, Mironov SA, Agaltsov MV, Ponomarenko VI, Karavaev AS, Drapkina OM, Penzel T. Directional couplings between the respiration and parasympathetic control of the heart rate during sleep and wakefulness in healthy subjects at different ages. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:942700. [PMID: 36926072 PMCID: PMC10013057 DOI: 10.3389/fnetp.2022.942700] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022]
Abstract
Cardiorespiratory interactions are important, both for understanding the fundamental processes of functioning of the human body and for development of methods for diagnostics of various pathologies. The properties of cardiorespiratory interaction are determined by the processes of autonomic control of blood circulation, which are modulated by the higher nervous activity. We study the directional couplings between the respiration and the process of parasympathetic control of the heart rate in the awake state and different stages of sleep in 96 healthy subjects from different age groups. The detection of directional couplings is carried out using the method of phase dynamics modeling applied to experimental RR-intervals and the signal of respiration. We reveal the presence of bidirectional couplings between the studied processes in all age groups. Our results show that the coupling from respiration to the process of parasympathetic control of the heart rate is stronger than the coupling in the opposite direction. The difference in the strength of bidirectional couplings between the considered processes is most pronounced in deep sleep.
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Affiliation(s)
- Ekaterina I Borovkova
- National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia
| | - Mikhail D Prokhorov
- Smart Sleep Laboratory, Saratov State University, Saratov, Russia.,Laboratory of Nonlinear Dynamics Modeling, Saratov Branch of Kotelnikov Institute of Radio Engineering and Electronics of Russian Academy of Sciences, Saratov, Russia
| | - Anton R Kiselev
- National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia.,Institute of Cardiological Research, Saratov State Medical University, Saratov, Russia
| | | | - Sergey A Mironov
- National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia
| | - Mikhail V Agaltsov
- National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia
| | - Vladimir I Ponomarenko
- Smart Sleep Laboratory, Saratov State University, Saratov, Russia.,Laboratory of Nonlinear Dynamics Modeling, Saratov Branch of Kotelnikov Institute of Radio Engineering and Electronics of Russian Academy of Sciences, Saratov, Russia
| | - Anatoly S Karavaev
- Smart Sleep Laboratory, Saratov State University, Saratov, Russia.,Laboratory of Nonlinear Dynamics Modeling, Saratov Branch of Kotelnikov Institute of Radio Engineering and Electronics of Russian Academy of Sciences, Saratov, Russia.,Institute of Cardiological Research, Saratov State Medical University, Saratov, Russia
| | - Oksana M Drapkina
- National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia
| | - Thomas Penzel
- Smart Sleep Laboratory, Saratov State University, Saratov, Russia.,Interdisciplinary Sleep Medicine Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
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32
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Papana A. Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1570. [PMID: 34945876 PMCID: PMC8700128 DOI: 10.3390/e23121570] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 12/16/2022]
Abstract
The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.
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Affiliation(s)
- Angeliki Papana
- Department of Economics, University of Macedonia, 54636 Thessaloniki, Greece
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33
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Ghiasi S, Dell'Acqua C, Benvenuti SM, Scilingo EP, Gentili C, Valenza G, Greco A. Classifying subclinical depression using EEG spectral and connectivity measures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2050-2053. [PMID: 34891691 DOI: 10.1109/embc46164.2021.9630044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Detecting depression on its early stages helps preventing the onset of severe depressive episodes. In this study, we propose an automatic classification pipeline to detect subclinical depression (i.e., dysphoria) through the electroencephalography (EEG) signal. To this aim, we recorded the EEG signals in resting condition from 26 female participants with dysphoria and 38 female controls. The EEG signals were processed to extract several spectral and functional connectivity features to feed a nonlinear Support Vector Machine (SVM) classifier embedded with a Recursive Feature Elimination (RFE) algorithm. Our recognition pipeline obtained a maximum classification accuracy of 83.91% in recognizing dysphoria patients with a combination of connectivity and spectral measures. Moreover, an accuracy of 76.11% was achieved with only the 4 most informative functional connections, suggesting a central role of cortical connectivity in the theta band for early depression recognition. The present study can facilitate the diagnosis of subclinical conditions of depression and may provide reliable indicators of depression for the clinical community.
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34
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Gross J, Kluger DS, Abbasi O, Chalas N, Steingräber N, Daube C, Schoffelen JM. Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis. Neuroimage 2021; 245:118660. [PMID: 34715317 DOI: 10.1016/j.neuroimage.2021.118660] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/28/2021] [Accepted: 10/15/2021] [Indexed: 12/31/2022] Open
Abstract
Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, WPPC and GCMI tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appears to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions.
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Affiliation(s)
- Joachim Gross
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, Münster, Germany; Otto-Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany; Centre for Cognitive Neuroimaging, University of Glasgow, Glasgow, UK
| | - Daniel S Kluger
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, Münster, Germany; Otto-Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany.
| | - Omid Abbasi
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, Münster, Germany
| | - Nikolas Chalas
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, Münster, Germany; Otto-Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
| | - Nadine Steingräber
- Institute for Biomagnetism and Biosignal Analysis, University of Münster, Münster, Germany
| | - Christoph Daube
- Centre for Cognitive Neuroimaging, University of Glasgow, Glasgow, UK
| | - Jan-Mathijs Schoffelen
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, NL, the Netherlands
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35
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Likens AD, Wiltshire TJ. Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics. Soc Cogn Affect Neurosci 2021; 16:232-245. [PMID: 32991716 PMCID: PMC7812625 DOI: 10.1093/scan/nsaa130] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 06/30/2020] [Accepted: 09/18/2020] [Indexed: 12/18/2022] Open
Abstract
Social interactions are pervasive in human life with varying forms of interpersonal coordination emerging and spanning different modalities (e.g. behaviors, speech/language, and neurophysiology). However, during social interactions, as in any dynamical system, patterns of coordination form and dissipate at different scales. Historically, researchers have used aggregate measures to capture coordination over time. While those measures (e.g. mean relative phase, cross-correlation, coherence) have provided a wealth of information about coordination in social settings, some evidence suggests that multiscale coordination may change over the time course of a typical empirical observation. To address this gap, we demonstrate an underutilized method, windowed multiscale synchrony, that moves beyond quantifying aggregate measures of coordination by focusing on how the relative strength of coordination changes over time and the scales that comprise social interaction. This method involves using a wavelet transform to decompose time series into component frequencies (i.e. scales), preserving temporal information and then quantifying phase synchronization at each of these scales. We apply this method to both simulated and empirical interpersonal physiological and neuromechanical data. We anticipate that demonstrating this method will stimulate new insights on the mechanisms and functions of synchrony in interpersonal contexts using neurophysiological and behavioral measures.
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Affiliation(s)
- Aaron D Likens
- Department of Biomechanics, University of Nebraska at Omaha, 6001 Dodge Street Omaha, NE 68182
| | - Travis J Wiltshire
- Department of Cognitive Science & Artificial Intelligence, Tilburg University, (Room D104) Warandelaan 2, 5037 AB, Tilburg, The Netherlands
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Mahadevan AS, Tooley UA, Bertolero MA, Mackey AP, Bassett DS. Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data. Neuroimage 2021; 241:118408. [PMID: 34284108 DOI: 10.1016/j.neuroimage.2021.118408] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 07/14/2021] [Accepted: 07/16/2021] [Indexed: 01/11/2023] Open
Abstract
Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.
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Affiliation(s)
- Arun S Mahadevan
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ursula A Tooley
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA 19104, USA
| | - Maxwell A Bertolero
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Allyson P Mackey
- Department of Psychology, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA.
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Puppo F, Pré D, Bang AG, Silva GA. Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks. Front Neurosci 2021; 15:647877. [PMID: 34335152 PMCID: PMC8323822 DOI: 10.3389/fnins.2021.647877] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 05/31/2021] [Indexed: 12/22/2022] Open
Abstract
Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective-direct and causal-connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect, and apparent links. Our method can be generally applied to the functional characterization of any in vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in vitro hiPSC-derived neuronal cultures.
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Affiliation(s)
- Francesca Puppo
- BioCircuits Institute and Center for Engineered Natural Intelligence, University of California, San Diego, La Jolla, CA, United States
| | - Deborah Pré
- Conrad Prebys Center for Chemical Genomics, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, United States
| | - Anne G. Bang
- Conrad Prebys Center for Chemical Genomics, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, United States
| | - Gabriel A. Silva
- BioCircuits Institute, Center for Engineered Natural Intelligence, Department of Bioengineering, Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
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Karavaev AS, Borovik AS, Borovkova EI, Orlova EA, Simonyan MA, Ponomarenko VI, Skazkina VV, Gridnev VI, Bezruchko BP, Prokhorov MD, Kiselev AR. Low-frequency component of photoplethysmogram reflects the autonomic control of blood pressure. Biophys J 2021; 120:2657-2664. [PMID: 34087217 PMCID: PMC8390904 DOI: 10.1016/j.bpj.2021.05.020] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 04/18/2021] [Accepted: 05/17/2021] [Indexed: 11/30/2022] Open
Abstract
The question of how much information the photoplethysmogram (PPG) signal contains on the autonomic regulation of blood pressure (BP) remains unsolved. This study aims to compare the low-frequency (LF) and high-frequency components of PPG and BP and assess their correlation with oscillations in interbeat (RR) intervals at similar frequencies. The PPG signal from the distal phalanx of the right index finger recorded using a reflective PPG sensor at green light, the BP signal from the left hand recorded using a Finometer, and RR intervals were analyzed. These signals were simultaneously recorded within 15 min in a supine resting condition in 17 healthy subjects (12 males and 5 females) aged 33 ± 9 years (mean ± SD). The study revealed the high coherence of LF components of PPG and BP with the LF component of RR intervals. The high-frequency components of these signals had low coherence. The analysis of the signal instantaneous phases revealed the presence of high-phase coherence between the LF components of PPG and BP. It is shown that the LF component of PPG is determined not only by local myogenic activity but also reflects the processes of autonomic control of BP.
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Affiliation(s)
- Anatoly S Karavaev
- Saratov State Medical University, Saratov, Russia; Saratov Branch of the Institute of Radio-Engineering and Electronics of Russian Academy of Sciences, Saratov, Russia; Saratov State University, Saratov, Russia
| | - Anatoly S Borovik
- Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, Russia
| | - Ekaterina I Borovkova
- Saratov State Medical University, Saratov, Russia; Saratov State University, Saratov, Russia
| | - Eugeniya A Orlova
- Institute of Biomedical Problems, Russian Academy of Sciences, Moscow, Russia
| | | | - Vladimir I Ponomarenko
- Saratov Branch of the Institute of Radio-Engineering and Electronics of Russian Academy of Sciences, Saratov, Russia; Saratov State University, Saratov, Russia
| | | | - Vladimir I Gridnev
- Saratov State Medical University, Saratov, Russia; Saratov State University, Saratov, Russia
| | - Boris P Bezruchko
- Saratov Branch of the Institute of Radio-Engineering and Electronics of Russian Academy of Sciences, Saratov, Russia; Saratov State University, Saratov, Russia
| | - Mikhail D Prokhorov
- Saratov Branch of the Institute of Radio-Engineering and Electronics of Russian Academy of Sciences, Saratov, Russia
| | - Anton R Kiselev
- Saratov State Medical University, Saratov, Russia; National Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia.
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Ponomarenko VI, Karavaev AS, Borovkova EI, Hramkov AN, Kiselev AR, Prokhorov MD, Penzel T. Decrease of coherence between the respiration and parasympathetic control of the heart rate with aging. CHAOS (WOODBURY, N.Y.) 2021; 31:073105. [PMID: 34340353 DOI: 10.1063/5.0056624] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 06/16/2021] [Indexed: 06/13/2023]
Abstract
The study of coordinated behavior between different systems of the human body provides useful information on the functioning of the body. The peculiarities of interaction and coordinated dynamics of the heart rate and respiration are of particular interest. We investigated the coherence of the processes of respiration and autonomic control of the heart rate for people of different ages in the awake state, in sleep with rapid eye movement, and in deep sleep. Our analysis revealed a monotonic decrease in the coherence of these processes with increasing age. This can be explained by age-related changes in the system of autonomic control of circulation. For all age groups, we found a qualitatively similar dynamics of the coherence between the studied processes during a transition from the awake state to sleep.
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Affiliation(s)
- V I Ponomarenko
- Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences, Saratov Branch, Zelyonaya Street, 38, Saratov 410019, Russia
| | - A S Karavaev
- Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences, Saratov Branch, Zelyonaya Street, 38, Saratov 410019, Russia
| | - E I Borovkova
- Institute of Physics, Saratov State University, Astrakhanskaya Street, 83, Saratov 410012, Russia
| | - A N Hramkov
- Institute of Physics, Saratov State University, Astrakhanskaya Street, 83, Saratov 410012, Russia
| | - A R Kiselev
- Institute of Cardiological Research, Saratov State Medical University, B. Kazachaya Street, 112, Saratov 410012, Russia
| | - M D Prokhorov
- Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences, Saratov Branch, Zelyonaya Street, 38, Saratov 410019, Russia
| | - T Penzel
- Institute of Physics, Saratov State University, Astrakhanskaya Street, 83, Saratov 410012, Russia
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EEG Synchronization-Parameters in Patients With Subcortical Arteriosclerotic Encephalopathy and Gait Disorder. J Clin Neurophysiol 2021; 38:331-339. [PMID: 32501954 DOI: 10.1097/wnp.0000000000000701] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE Subcortical arteriosclerotic encephalopathy (SAE) is characterized by extensive white matter lesions in the MRI. Clinical symptoms are cognitive impairment, ranging from mild deficits to vascular dementia, impaired executive functioning, and gait disorders. In the EEG of SAE patients with vascular dementia, the lower frequencies are increased. However, it is unclear whether EEG changes also exist in SAE patients with gait disorders but without vascular dementia. METHODS The authors analyzed the EEGs of 50 nondemented patients with SAE and gait disorders and 50 healthy controls applying pointwise transinformation as a measure of synchronization. RESULTS Hundred seconds of waking EEG that appeared unaltered in visual analysis were sufficient to prove changes in synchronization. The authors found a decrease in the mean level of synchronization, combined with an elongation of synchronization time in all examined brain areas. These effects correlated slightly with the extent of subcortical lesions. CONCLUSIONS Changes in EEG synchronization in patients with SAE and gait disorders seem to occur independently of cognitive function. The causal relationship of the changes in EEG synchronization and gait disorders remains to be clarified. The results of this study might point to a decrease in coupling efficiency in these patients, with the increase in synchronization duration as a possible compensatory mechanism. Because a time-efficient signal transmission particularly during gait execution is crucial, reduced efficiency might contribute to an impairment of postural stabilization. The study results might indicate a neuronal network for planning and execution of motor activity and particularly gait, extending from the frontal over the central to the parietal cortex.
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Smith RJ, Alipourjeddi E, Garner C, Maser AL, Shrey DW, Lopour BA. Infant functional networks are modulated by state of consciousness and circadian rhythm. Netw Neurosci 2021; 5:614-630. [PMID: 34189380 PMCID: PMC8233111 DOI: 10.1162/netn_a_00194] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 03/22/2021] [Indexed: 01/05/2023] Open
Abstract
Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.
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Affiliation(s)
- Rachel J. Smith
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
| | - Ehsan Alipourjeddi
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
| | - Cristal Garner
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Amy L. Maser
- Department of Psychology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Daniel W. Shrey
- Division of Neurology, Children’s Hospital of Orange County, Orange, CA, USA
- Department of Pediatrics, University of California, Irvine, Irvine, CA, USA
| | - Beth A. Lopour
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
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Budzinski R, Lopes S, Masoller C. Symbolic analysis of bursting dynamical regimes of Rulkov neural networks. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.05.122] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Gupta K, Paluš M. Cross-Scale Causality and Information Transfer in Simulated Epileptic Seizures. ENTROPY 2021; 23:e23050526. [PMID: 33923035 PMCID: PMC8146730 DOI: 10.3390/e23050526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/22/2021] [Accepted: 04/23/2021] [Indexed: 12/04/2022]
Abstract
An information-theoretic approach for detecting causality and information transfer was applied to phases and amplitudes of oscillatory components related to different time scales and obtained using the wavelet transform from a time series generated by the Epileptor model. Three main time scales and their causal interactions were identified in the simulated epileptic seizures, in agreement with the interactions of the model variables. An approach consisting of wavelet transform, conditional mutual information estimation, and surrogate data testing applied to a single time series generated by the model was demonstrated to be successful in the identification of all directional (causal) interactions between the three different time scales described in the model. Thus, the methodology was prepared for the identification of causal cross-frequency phase–phase and phase–amplitude interactions in experimental and clinical neural data.
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Affiliation(s)
| | - Milan Paluš
- Correspondence: ; Tel.: +420-266-053-430; Fax: +420-286-585-789
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Kankanamge D, Ubeysinghe S, Tennakoon M, Pantula PD, Mitra K, Giri L, Karunarathne A. Dissociation of the G protein βγ from the Gq-PLCβ complex partially attenuates PIP2 hydrolysis. J Biol Chem 2021; 296:100702. [PMID: 33901492 PMCID: PMC8138763 DOI: 10.1016/j.jbc.2021.100702] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 04/09/2021] [Accepted: 04/21/2021] [Indexed: 01/14/2023] Open
Abstract
Phospholipase C β (PLCβ), which is activated by the Gq family of heterotrimeric G proteins, hydrolyzes the inner membrane lipid phosphatidylinositol 4,5-bisphosphate (PIP2), generating diacylglycerol and inositol 1,4,5-triphosphate (IP3). Because Gq and PLCβ regulate many crucial cellular processes and have been identified as major disease drivers, activation and termination of PLCβ signaling by the Gαq subunit have been extensively studied. Gq-coupled receptor activation induces intense and transient PIP2 hydrolysis, which subsequently recovers to a low-intensity steady-state equilibrium. However, the molecular underpinnings of this equilibrium remain unclear. Here, we explored the influence of signaling crosstalk between Gq and Gi/o pathways on PIP2 metabolism in living cells using single-cell and optogenetic approaches to spatially and temporally constrain signaling. Our data suggest that the Gβγ complex is a component of the highly efficient lipase GαqGTP-PLCβ-Gβγ. We found that over time, Gβγ dissociates from this lipase complex, leaving the less-efficient GαqGTP-PLCβ lipase complex and allowing the significant partial recovery of PIP2 levels. Our findings also indicate that the subtype of the Gγ subunit in Gβγ fine-tunes the lipase activity of Gq-PLCβ, in which cells expressing Gγ with higher plasma membrane interaction show lower PIP2 recovery. Given that Gγ shows cell- and tissue-specific subtype expression, our findings suggest the existence of tissue-specific distinct Gq-PLCβ signaling paradigms. Furthermore, these results also outline a molecular process that likely safeguards cells from excessive Gq signaling.
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Affiliation(s)
- Dinesh Kankanamge
- Department of Chemistry and Biochemistry, The University of Toledo, Toledo, Ohio, USA
| | - Sithurandi Ubeysinghe
- Department of Chemistry and Biochemistry, The University of Toledo, Toledo, Ohio, USA
| | - Mithila Tennakoon
- Department of Chemistry and Biochemistry, The University of Toledo, Toledo, Ohio, USA
| | - Priyanka Devi Pantula
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, Sangareddy, Telangana, India
| | - Kishalay Mitra
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, Sangareddy, Telangana, India
| | - Lopamudra Giri
- Department of Chemical Engineering, Indian Institute of Technology, Hyderabad, Sangareddy, Telangana, India
| | - Ajith Karunarathne
- Department of Chemistry and Biochemistry, The University of Toledo, Toledo, Ohio, USA.
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Fast and effective pseudo transfer entropy for bivariate data-driven causal inference. Sci Rep 2021; 11:8423. [PMID: 33875707 PMCID: PMC8055902 DOI: 10.1038/s41598-021-87818-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/30/2021] [Indexed: 11/08/2022] Open
Abstract
Identifying, from time series analysis, reliable indicators of causal relationships is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years many methods for data-driven causal inference have been proposed; however, their success largely depends on the characteristics of the system under investigation. Often, their data requirements, computational cost or number of parameters limit their applicability. Here we propose a computationally efficient measure for causality testing, which we refer to as pseudo transfer entropy (pTE), that we derive from the standard definition of transfer entropy (TE) by using a Gaussian approximation. We demonstrate the power of the pTE measure on simulated and on real-world data. In all cases we find that pTE returns results that are very similar to those returned by Granger causality (GC). Importantly, for short time series, pTE combined with time-shifted (T-S) surrogates for significance testing strongly reduces the computational cost with respect to the widely used iterative amplitude adjusted Fourier transform (IAAFT) surrogate testing. For example, for time series of 100 data points, pTE and T-S reduce the computational time by \documentclass[12pt]{minimal}
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\begin{document}$$82\%$$\end{document}82% with respect to GC and IAAFT. We also show that pTE is robust against observational noise. Therefore, we argue that the causal inference approach proposed here will be extremely valuable when causality networks need to be inferred from the analysis of a large number of short time series.
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Antagonistic muscle prefatigue weakens the functional corticomuscular coupling during isometric elbow extension contraction. Neuroreport 2021; 31:372-380. [PMID: 31876688 DOI: 10.1097/wnr.0000000000001387] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE During muscle fatigue, acute changes in the interaction between the sensorimotor cortex and peripheral neurons have been widely studied. However, it is still unclear about the effect of antagonist muscle prefatigue on corticomuscular coupling and central modulation. The purpose of this study was to investigate the changes in the magnitude of electroencephalogram-electromyography (EEG-EMG) coherence and phase synchronization index (PSI) induced by antagonistic muscle prefatigue. METHODS Twelve young male volunteers conducted a 30-s long, nonfatiguing isometric elbow extension with a target force level of 20% maximum voluntary contraction (MVC) before and after a fatiguing sustained elbow flexion contraction at 20% MVC until task failure. Coherence and PSI between the EEG recorded over the sensorimotor cortex and the surface EMG of the triceps brachii (TB) muscle were quantified for the pre- and post-fatigue elbow extension contractions. RESULTS Coherence and PSI in the gamma frequency band (35-60 Hz) were found significantly decreased in the postfatigue elbow extension contraction than the prefatigue contraction. The power of the EEG in the beta and gamma band were significantly increased, while the EMG power showed no significant changes when the antagonistic muscle was prefatigued. PSI in the gamma frequency band between the EMG of the TB muscle and the EEG were found significantly decreased during postfatigue elbow extension contraction compared with prefatigue contraction. CONCLUSION Antagonistic muscle prefatigue led to significantly lower gamma band corticomuscular coherence and phase coupling during an isometric elbow extension position task. The lower corticomuscular coupling may indicate a central modulation mechanism of antagonist muscle prefatigue that related to decreased descending common drive or joint instability compensation modulation mechanism.
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Deco G, Vidaurre D, Kringelbach ML. Revisiting the global workspace orchestrating the hierarchical organization of the human brain. Nat Hum Behav 2021; 5:497-511. [PMID: 33398141 PMCID: PMC8060164 DOI: 10.1038/s41562-020-01003-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 10/12/2020] [Indexed: 12/31/2022]
Abstract
A central challenge in neuroscience is how the brain organizes the information necessary to orchestrate behaviour. Arguably, this whole-brain orchestration is carried out by a core subset of integrative brain regions, a 'global workspace', but its constitutive regions remain unclear. We quantified the global workspace as the common regions across seven tasks as well as rest, in a common 'functional rich club'. To identify this functional rich club, we determined the information flow between brain regions by means of a normalized directed transfer entropy framework applied to multimodal neuroimaging data from 1,003 healthy participants and validated in participants with retest data. This revealed a set of regions orchestrating information from perceptual, long-term memory, evaluative and attentional systems. We confirmed the causal significance and robustness of our results by systematically lesioning a generative whole-brain model. Overall, this framework describes a complex choreography of the functional hierarchical organization of the human brain.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- School of Psychological Sciences, Monash University, Clayton, Victoria, Australia.
| | - Diego Vidaurre
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK.
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
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Causality and Information Transfer Between the Solar Wind and the Magnetosphere-Ionosphere System. ENTROPY 2021; 23:e23040390. [PMID: 33806048 PMCID: PMC8064447 DOI: 10.3390/e23040390] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 03/19/2021] [Accepted: 03/22/2021] [Indexed: 11/16/2022]
Abstract
An information-theoretic approach for detecting causality and information transfer is used to identify interactions of solar activity and interplanetary medium conditions with the Earth's magnetosphere-ionosphere systems. A causal information transfer from the solar wind parameters to geomagnetic indices is detected. The vertical component of the interplanetary magnetic field (Bz) influences the auroral electrojet (AE) index with an information transfer delay of 10 min and the geomagnetic disturbances at mid-latitudes measured by the symmetric field in the H component (SYM-H) index with a delay of about 30 min. Using a properly conditioned causality measure, no causal link between AE and SYM-H, or between magnetospheric substorms and magnetic storms can be detected. The observed causal relations can be described as linear time-delayed information transfer.
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Huang H, Zhang J, Zhu L, Tang J, Lin G, Kong W, Lei X, Zhu L. EEG-Based Sleep Staging Analysis with Functional Connectivity. SENSORS (BASEL, SWITZERLAND) 2021; 21:1988. [PMID: 33799850 PMCID: PMC7999974 DOI: 10.3390/s21061988] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/26/2021] [Accepted: 03/08/2021] [Indexed: 12/20/2022]
Abstract
Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.
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Affiliation(s)
- Hui Huang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Jianhai Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Li Zhu
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Jiajia Tang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Guang Lin
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; (H.H.); (J.Z.); (J.T.); (G.L.)
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Wanzeng Kong
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou Dianzi University, Hangzhou 310018, China;
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China;
- Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing 400715, China
| | - Lei Zhu
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China;
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Clinical application of intraoperative trial-free online-based language mapping for patients with refractory epilepsy. Epilepsy Behav 2021; 116:107496. [PMID: 33582498 DOI: 10.1016/j.yebeh.2020.107496] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/10/2020] [Accepted: 09/11/2020] [Indexed: 11/22/2022]
Abstract
OBJECTIVE The objective of the study was to develop and clinically test a trial-free online-based language mapping method for localizing the eloquent cortex easily in epilepsy operation. METHODS Nine patients with refractory epilepsy were included in this study according to the results of preoperative evaluation for their epileptogenic zones (EZs) located adjacent to the eloquent cortex. When patients were awakened up from general anesthesia during operation, the trial-free online-based language-mapping paradigm was performed. All positive points marked on the cortex in each test were labeled and superimposed together as the result of functional mapping for each patient. The eloquent cortex was mapped according to the results obtained both from the intraoperative trial-free task localization method and the traditional electrical cortical stimulation (ECS). RESULTS All patients completed this paradigms twice within 10 min. Based on the results of mapping, the EZs were tried to fully resected on the premise of preserving the mapped eloquent cortex as much as possible. The postoperative follow-up showed the outcome of Engel I in six patients and Engel II in three patients, whereas only two patients had aphemia after surgery and recovered within one week and three months, respectively. SIGNIFICANCE The intraoperative trial-free online-based language mapping method was primarily identified to be safe and effective. This novel method seems to be promising and worthy of improvement.
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