Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity
Recently, non-linear statistical measures such as multi-scale entropy (MSE) have been introduced as indices of the complexity of electrophysiology and fMRI time-series across multiple time scales. In this work, we investigated the neurophysiological underpinnings of complexity (MSE) of electrophysio...
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| Format: | Article |
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Frontiers Media S.A.
2018-05-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2018.00352/full |
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| author | Danny J. J. Wang Kay Jann Chang Fan Yang Qiao Yang Qiao Yu-Feng Zang Hanbing Lu Yihong Yang |
| author_facet | Danny J. J. Wang Kay Jann Chang Fan Yang Qiao Yang Qiao Yu-Feng Zang Hanbing Lu Yihong Yang |
| author_sort | Danny J. J. Wang |
| collection | DOAJ |
| description | Recently, non-linear statistical measures such as multi-scale entropy (MSE) have been introduced as indices of the complexity of electrophysiology and fMRI time-series across multiple time scales. In this work, we investigated the neurophysiological underpinnings of complexity (MSE) of electrophysiology and fMRI signals and their relations to functional connectivity (FC). MSE and FC analyses were performed on simulated data using neural mass model based brain network model with the Brain Dynamics Toolbox, on animal models with concurrent recording of fMRI and electrophysiology in conjunction with pharmacological manipulations, and on resting-state fMRI data from the Human Connectome Project. Our results show that the complexity of regional electrophysiology and fMRI signals is positively correlated with network FC. The associations between MSE and FC are dependent on the temporal scales or frequencies, with higher associations between MSE and FC at lower temporal frequencies. Our results from theoretical modeling, animal experiment and human fMRI indicate that (1) Regional neural complexity and network FC may be two related aspects of brain's information processing: the more complex regional neural activity, the higher FC this region has with other brain regions; (2) MSE at high and low frequencies may represent local and distributed information processing across brain regions. Based on literature and our data, we propose that the complexity of regional neural signals may serve as an index of the brain's capacity of information processing—increased complexity may indicate greater transition or exploration between different states of brain networks, thereby a greater propensity for information processing. |
| first_indexed | 2024-12-11T01:18:07Z |
| format | Article |
| id | doaj.art-e97df784c13c4eef861b08ec13548646 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-11T01:18:07Z |
| publishDate | 2018-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-e97df784c13c4eef861b08ec135486462022-12-22T01:25:48ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-05-011210.3389/fnins.2018.00352356298Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional ConnectivityDanny J. J. Wang0Kay Jann1Chang Fan2Yang Qiao3Yang Qiao4Yu-Feng Zang5Hanbing Lu6Yihong Yang7Laboratory of FMRI Technology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United StatesLaboratory of FMRI Technology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United StatesLaboratory of FMRI Technology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United StatesDepartment of Psychology, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, ChinaNeuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United StatesDepartment of Psychology, Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, ChinaNeuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United StatesNeuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, United StatesRecently, non-linear statistical measures such as multi-scale entropy (MSE) have been introduced as indices of the complexity of electrophysiology and fMRI time-series across multiple time scales. In this work, we investigated the neurophysiological underpinnings of complexity (MSE) of electrophysiology and fMRI signals and their relations to functional connectivity (FC). MSE and FC analyses were performed on simulated data using neural mass model based brain network model with the Brain Dynamics Toolbox, on animal models with concurrent recording of fMRI and electrophysiology in conjunction with pharmacological manipulations, and on resting-state fMRI data from the Human Connectome Project. Our results show that the complexity of regional electrophysiology and fMRI signals is positively correlated with network FC. The associations between MSE and FC are dependent on the temporal scales or frequencies, with higher associations between MSE and FC at lower temporal frequencies. Our results from theoretical modeling, animal experiment and human fMRI indicate that (1) Regional neural complexity and network FC may be two related aspects of brain's information processing: the more complex regional neural activity, the higher FC this region has with other brain regions; (2) MSE at high and low frequencies may represent local and distributed information processing across brain regions. Based on literature and our data, we propose that the complexity of regional neural signals may serve as an index of the brain's capacity of information processing—increased complexity may indicate greater transition or exploration between different states of brain networks, thereby a greater propensity for information processing.https://www.frontiersin.org/article/10.3389/fnins.2018.00352/fullmultiscale entropy (MSE)complexityBOLD fMRIelectrophysiologyfunctional connectivity (FC) |
| spellingShingle | Danny J. J. Wang Kay Jann Chang Fan Yang Qiao Yang Qiao Yu-Feng Zang Hanbing Lu Yihong Yang Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity Frontiers in Neuroscience multiscale entropy (MSE) complexity BOLD fMRI electrophysiology functional connectivity (FC) |
| title | Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity |
| title_full | Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity |
| title_fullStr | Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity |
| title_full_unstemmed | Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity |
| title_short | Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship With Functional Connectivity |
| title_sort | neurophysiological basis of multi scale entropy of brain complexity and its relationship with functional connectivity |
| topic | multiscale entropy (MSE) complexity BOLD fMRI electrophysiology functional connectivity (FC) |
| url | https://www.frontiersin.org/article/10.3389/fnins.2018.00352/full |
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