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Zhang H, Zhou QQ, Chen H, Hu XQ, Li WG, Bai Y, Han JX, Wang Y, Liang ZH, Chen D, Cong FY, Yan JQ, Li XL. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Mil Med Res 2023; 10:67. [PMID: 38115158 PMCID: PMC10729551 DOI: 10.1186/s40779-023-00502-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/23/2023] [Indexed: 12/21/2023] Open
Abstract
Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.
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Affiliation(s)
- Hao Zhang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Qing-Qi Zhou
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China
| | - He Chen
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China
| | - Xiao-Qing Hu
- Department of Psychology, the State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, 999077, China
- HKU-Shenzhen Institute of Research and Innovation, Shenzhen, 518057, Guangdong, China
| | - Wei-Guang Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Yang Bai
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang, 330006, China
| | - Jun-Xia Han
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yao Wang
- School of Communication Science, Beijing Language and Culture University, Beijing, 100083, China
| | - Zhen-Hu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.
| | - Dan Chen
- School of Computer Science, Wuhan University, Wuhan, 430072, China.
| | - Feng-Yu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116081, Liaoning, China.
| | - Jia-Qing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China.
| | - Xiao-Li Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China.
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2
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Montupil J, Cardone P, Staquet C, Bonhomme A, Defresne A, Martial C, Alnagger NL, Gosseries O, Bonhomme V. The nature of consciousness in anaesthesia. BJA OPEN 2023; 8:100224. [PMID: 37780201 PMCID: PMC10539891 DOI: 10.1016/j.bjao.2023.100224] [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: 08/22/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023]
Abstract
Neuroscientists agree on the value of locating the source of consciousness within the brain. Anaesthesiologists are no exception, and have their own operational definition of consciousness based on phenomenological observations during anaesthesia. The full functional correlates of consciousness are yet to be precisely identified, however rapidly evolving progress in this scientific domain has yielded several theories that attempt to model the generation of consciousness. They have received variable support from experimental observations, including those involving anaesthesia and its ability to reversibly modulate different aspects of consciousness. Aside from the interest in a better understanding of the mechanisms of consciousness, exploring the functional tenets of the phenomenological consciousness states of general anaesthesia has the potential to ultimately improve patient management. It could facilitate the design of specific monitoring devices and approaches, aiming at reliably detecting each of the possible states of consciousness during an anaesthetic procedure, including total absence of mental content (unconsciousness), and internal awareness (sensation of self and internal thoughts) with or without conscious perception of the environment (connected or disconnected consciousness, respectively). Indeed, it must be noted that unresponsiveness is not sufficient to infer absence of connectedness or even absence of consciousness. This narrative review presents the current knowledge in this field from a system-level, underlining the contribution of anaesthesia studies in supporting theories of consciousness, and proposing directions for future research.
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Affiliation(s)
- Javier Montupil
- Anesthesia and Perioperative Neuroscience Laboratory, Liege, Belgium
- Department of Anesthesia and Intensive Care Medicine, Liege, Belgium
- University Department of Anesthesia and Intensive Care Medicine, Citadelle Regional Hospital, Liege, Belgium
| | - Paolo Cardone
- Coma Science Group, GIGA-Consciousness Thematic Unit, GIGA-Research, Liege University, Liege, Belgium
- Centre du Cerveau, Liege University Hospital, Liege, Belgium
| | - Cécile Staquet
- Anesthesia and Perioperative Neuroscience Laboratory, Liege, Belgium
- Department of Anesthesia and Intensive Care Medicine, Liege, Belgium
| | - Arthur Bonhomme
- Coma Science Group, GIGA-Consciousness Thematic Unit, GIGA-Research, Liege University, Liege, Belgium
| | - Aline Defresne
- Anesthesia and Perioperative Neuroscience Laboratory, Liege, Belgium
- Department of Anesthesia and Intensive Care Medicine, Liege, Belgium
- University Department of Anesthesia and Intensive Care Medicine, Citadelle Regional Hospital, Liege, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA-Consciousness Thematic Unit, GIGA-Research, Liege University, Liege, Belgium
- Centre du Cerveau, Liege University Hospital, Liege, Belgium
| | - Naji L.N. Alnagger
- Coma Science Group, GIGA-Consciousness Thematic Unit, GIGA-Research, Liege University, Liege, Belgium
- Centre du Cerveau, Liege University Hospital, Liege, Belgium
| | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness Thematic Unit, GIGA-Research, Liege University, Liege, Belgium
- Centre du Cerveau, Liege University Hospital, Liege, Belgium
| | - Vincent Bonhomme
- Anesthesia and Perioperative Neuroscience Laboratory, Liege, Belgium
- Department of Anesthesia and Intensive Care Medicine, Liege, Belgium
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He S, Li Y, Le X, Han X, Lin J, Peng X, Li M, Yang R, Yao D, Valdes-Sosa PA, Ren P. Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2023; 31:1764-1775. [PMID: 37030736 DOI: 10.1109/tnsre.2023.3260143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
OBJECTIVE Multivariate signal (MS) analysis, especially the assessment of its information transmission (for example, from the perspective of network science), is the key to our understanding of various phenomena in biology, physics and economics. Although there is a large amount of literature demonstrating that MS can be decomposed into space-time-frequency domain information, there seems to be no research confirming that multivariate information transmission (MIT) in these three domains can be quantified. Therefore, in this study, we attempted to combine dynamic mode decomposition (DMD) and parallel communication model (PCM) together to realize it. METHODS We first regarded MS as a large-scale system and then used DMD to decompose it into specific subsystems with their own intrinsic oscillatory frequencies. At the same time, the transition probability matrix (TPM) of information transmission within and between MS at two consecutive moments in each subsystem can also be calculated. Then, communication parameters (CPs) derived from each TPM were calculated in order to quantify the MIT in the space-time-frequency domain. In this study, multidimensional electroencephalogram (EEG) signals were used to illustrate our method. RESULTS Compared with traditional EEG brain networks, this method shows greater potential in EEG analysis to distinguish between patients and healthy controls. CONCLUSION This study demonstrates the feasibility of measuring MIT in the space-time-frequency domain simultaneously. SIGNIFICANCE This study shows that MIT analysis in the space-time-frequency domain is not only completely different from the MS decomposition in these three domains, but also can reveal many new phenomena behind MS that have not yet been discovered.
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Altered functional and directed connectivity in propofol-induced loss of consciousness: A source-space resting-state EEG study. Clin Neurophysiol 2022; 142:209-219. [DOI: 10.1016/j.clinph.2022.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 07/26/2022] [Accepted: 08/01/2022] [Indexed: 11/19/2022]
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5
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González C, Garcia-Hernando G, Jensen EW, Vallverdú-Ferrer M. Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:912733. [PMID: 36926077 PMCID: PMC10013012 DOI: 10.3389/fnetp.2022.912733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022]
Abstract
Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.
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Affiliation(s)
- Carmen González
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain.,Research and Development Department, Quantium Medical, Mataró, Spain
| | - Gabriel Garcia-Hernando
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain.,Research and Development Department, Quantium Medical, Mataró, Spain
| | - Erik W Jensen
- Research and Development Department, Quantium Medical, Mataró, Spain
| | - Montserrat Vallverdú-Ferrer
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain
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Sattin D, Duran D, Visintini S, Schiaffi E, Panzica F, Carozzi C, Rossi Sebastiano D, Visani E, Tobaldini E, Carandina A, Citterio V, Magnani FG, Cacciatore M, Orena E, Montano N, Caldiroli D, Franceschetti S, Picozzi M, Matilde L. Analyzing the Loss and the Recovery of Consciousness: Functional Connectivity Patterns and Changes in Heart Rate Variability During Propofol-Induced Anesthesia. Front Syst Neurosci 2021; 15:652080. [PMID: 33889078 PMCID: PMC8055941 DOI: 10.3389/fnsys.2021.652080] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 03/15/2021] [Indexed: 11/13/2022] Open
Abstract
The analysis of the central and the autonomic nervous systems (CNS, ANS) activities during general anesthesia (GA) provides fundamental information for the study of neural processes that support alterations of the consciousness level. In the present pilot study, we analyzed EEG signals and the heart rate (HR) variability (HRV) in a sample of 11 patients undergoing spinal surgery to investigate their CNS and ANS activities during GA obtained with propofol administration. Data were analyzed during different stages of GA: baseline, the first period of anesthetic induction, the period before the loss of consciousness, the first period after propofol discontinuation, and the period before the recovery of consciousness (ROC). In EEG spectral analysis, we found a decrease in posterior alpha and beta power in all cortical areas observed, except the occipital ones, and an increase in delta power, mainly during the induction phase. In EEG connectivity analysis, we found a significant increase of local efficiency index in alpha and delta bands between baseline and loss of consciousness as well as between baseline and ROC in delta band only and a significant reduction of the characteristic path length in alpha band between the baseline and ROC. Moreover, connectivity results showed that in the alpha band there was mainly a progressive increase in the number and in the strength of incoming connections in the frontal region, while in the beta band the parietal region showed mainly a significant increase in the number and in the strength of outcoming connections values. The HRV analysis showed that the induction of anesthesia with propofol was associated with a progressive decrease in complexity and a consequent increase in the regularity indexes and that the anesthetic procedure determined bradycardia which was accompanied by an increase in cardiac sympathetic modulation and a decrease in cardiac parasympathetic modulation during the induction. Overall, the results of this pilot study showed as propofol-induced anesthesia caused modifications on EEG signal, leading to a "rebalance" between long and short-range cortical connections, and had a direct effect on the cardiac system. Our data suggest interesting perspectives for the interactions between the central and autonomic nervous systems for the modulation of the consciousness level.
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Affiliation(s)
- Davide Sattin
- Neurology, Public Health, Disability Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
- Clinical and Experimental Medicine and Medical Humanities-PhD Program, Insubria University, Varese, Italy
| | - Dunja Duran
- Clinical and Experimental Epileptology Division, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Sergio Visintini
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Elena Schiaffi
- Neurophysiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Ferruccio Panzica
- Clinical Engineering Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Carla Carozzi
- Department of Anaesthesia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Elisa Visani
- Clinical and Experimental Epileptology Division, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Eleonora Tobaldini
- Department of Internal Medicine, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Angelica Carandina
- Department of Internal Medicine, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Valeria Citterio
- Department of Internal Medicine, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Francesca Giulia Magnani
- Neurology, Public Health, Disability Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Martina Cacciatore
- Neurology, Public Health, Disability Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Eleonora Orena
- Department of Anaesthesia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Nicola Montano
- Department of Internal Medicine, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
| | - Dario Caldiroli
- Department of Anaesthesia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Silvana Franceschetti
- Neurophysiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Mario Picozzi
- Center for Clinical Ethics, Biotechnology and Life Sciences Department, Insubria University, Varese, Italy
| | - Leonardi Matilde
- Neurology, Public Health, Disability Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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Pullon RM, Yan L, Sleigh JW, Warnaby CE. Granger Causality of the Electroencephalogram Reveals Abrupt Global Loss of Cortical Information Flow during Propofol-induced Loss of Responsiveness. Anesthesiology 2020; 133:774-786. [PMID: 32930729 PMCID: PMC7495984 DOI: 10.1097/aln.0000000000003398] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
It is a commonly held view that information flow between widely separated regions of the cerebral cortex is a necessary component in the generation of wakefulness (also termed “connected” consciousness). This study therefore hypothesized that loss of wakefulness caused by propofol anesthesia should be associated with loss of information flow, as estimated by the effective connectivity in the scalp electroencephalogram (EEG) signal. In healthy adult volunteers, propofol anesthesia–induced loss of consciousness was associated with an abrupt, substantial, and global decrease in connectivity. These changes are comparably reversed at regain of consciousness. These observations suggest that information flow is an important indicator of wakefulness. Supplemental Digital Content is available in the text.
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8
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Chen X, Chen J, Cheng G, Gong T. Topics and trends in artificial intelligence assisted human brain research. PLoS One 2020; 15:e0231192. [PMID: 32251489 PMCID: PMC7135272 DOI: 10.1371/journal.pone.0231192] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 03/18/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AI-assisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AI-assisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AI-assisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.
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Affiliation(s)
- Xieling Chen
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China
| | - Juan Chen
- Center for the Study of Applied Psychology, Guangdong Key Laboratory of Mental Health and Cognitive Science and the School of Psychology, South China Normal University, Guangzhou, China
| | - Gary Cheng
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China
- * E-mail: (GC); (TG)
| | - Tao Gong
- Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies, Guangzhou, China
- Educational Testing Service, Princeton, NJ, United States of America
- * E-mail: (GC); (TG)
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9
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Bonhomme V, Staquet C, Montupil J, Defresne A, Kirsch M, Martial C, Vanhaudenhuyse A, Chatelle C, Larroque SK, Raimondo F, Demertzi A, Bodart O, Laureys S, Gosseries O. General Anesthesia: A Probe to Explore Consciousness. Front Syst Neurosci 2019; 13:36. [PMID: 31474839 PMCID: PMC6703193 DOI: 10.3389/fnsys.2019.00036] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 07/24/2019] [Indexed: 12/24/2022] Open
Abstract
General anesthesia reversibly alters consciousness, without shutting down the brain globally. Depending on the anesthetic agent and dose, it may produce different consciousness states including a complete absence of subjective experience (unconsciousness), a conscious experience without perception of the environment (disconnected consciousness, like during dreaming), or episodes of oriented consciousness with awareness of the environment (connected consciousness). Each consciousness state may potentially be followed by explicit or implicit memories after the procedure. In this respect, anesthesia can be considered as a proxy to explore consciousness. During the recent years, progress in the exploration of brain function has allowed a better understanding of the neural correlates of consciousness, and of their alterations during anesthesia. Several changes in functional and effective between-region brain connectivity, consciousness network topology, and spatio-temporal dynamics of between-region interactions have been evidenced during anesthesia. Despite a set of effects that are common to many anesthetic agents, it is still uneasy to draw a comprehensive picture of the precise cascades during general anesthesia. Several questions remain unsolved, including the exact identification of the neural substrate of consciousness and its components, the detection of specific consciousness states in unresponsive patients and their associated memory processes, the processing of sensory information during anesthesia, the pharmacodynamic interactions between anesthetic agents, the direction-dependent hysteresis phenomenon during the transitions between consciousness states, the mechanisms of cognitive alterations that follow an anesthetic procedure, the identification of an eventual unitary mechanism of anesthesia-induced alteration of consciousness, the relationship between network effects and the biochemical or sleep-wake cycle targets of anesthetic agents, as well as the vast between-studies variations in dose and administration mode, leading to difficulties in between-studies comparisons. In this narrative review, we draw the picture of the current state of knowledge in anesthesia-induced unconsciousness, from insights gathered on propofol, halogenated vapors, ketamine, dexmedetomidine, benzodiazepines and xenon. We also describe how anesthesia can help understanding consciousness, we develop the above-mentioned unresolved questions, and propose tracks for future research.
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Affiliation(s)
- Vincent Bonhomme
- Anesthesia and Intensive Care Laboratory, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium.,University Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Régional de la Citadelle (CHR Citadelle), Liege, Belgium.,Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Universitaire de Liège (CHU Lièege), Liege, Belgium
| | - Cécile Staquet
- Anesthesia and Intensive Care Laboratory, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium.,Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Universitaire de Liège (CHU Lièege), Liege, Belgium
| | - Javier Montupil
- Anesthesia and Intensive Care Laboratory, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium.,University Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Régional de la Citadelle (CHR Citadelle), Liege, Belgium.,Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Universitaire de Liège (CHU Lièege), Liege, Belgium
| | - Aline Defresne
- Anesthesia and Intensive Care Laboratory, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium.,University Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Régional de la Citadelle (CHR Citadelle), Liege, Belgium.,Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Universitaire de Liège (CHU Lièege), Liege, Belgium
| | - Murielle Kirsch
- Anesthesia and Intensive Care Laboratory, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium.,Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier Universitaire de Liège (CHU Lièege), Liege, Belgium
| | - Charlotte Martial
- Coma Science Group, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium
| | - Audrey Vanhaudenhuyse
- Sensation & Perception Research Group, GIGA-Consciousness, Department of Algology, GIGA Institute, University of Liege, Centre Hospitalier Universitaire de Liège (CHU Lièege), Liege, Belgium
| | - Camille Chatelle
- Coma Science Group, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium
| | - Stephen Karl Larroque
- Coma Science Group, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium
| | - Federico Raimondo
- Coma Science Group, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium
| | - Athena Demertzi
- Physiology of Cognition Research Lab, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium
| | - Olivier Bodart
- Coma Science Group, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium
| | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness, GIGA Institute, University of Liege, Liege, Belgium
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10
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Hejazi M, Motie Nasrabadi A. Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods. Cogn Neurodyn 2019; 13:461-473. [PMID: 31565091 DOI: 10.1007/s11571-019-09534-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 02/07/2019] [Accepted: 04/25/2019] [Indexed: 01/09/2023] Open
Abstract
Epilepsy is a chronic disorder, which causes strange perceptions, muscle spasms, sometimes seizures, and loss of awareness, associated with abnormal neuronal activity in the brain. The goal of this study is to investigate how effective connectivity (EC) changes effect on unexpected seizures prediction, as this will authorize the patients to play it safe and avoid risk. We approve the hypothesis that EC variables near seizure change significantly so seizure can be predicted in accordance with this variation. We introduce two time-variant coefficients based on standard deviation of EC on Freiburg EEG dataset by using directed transfer function and Granger causality methods and compare index changes over the course of time in five different frequency bands. Comparison of the multivariate and bivariate analysis of factors is implemented in this investigation. The performance based on the suggested methods shows the seizure occurrence period is approximately 50 min that is expected onset stated in, the maximum value of sensitivity approaching ~ 80%, and 0.33 FP/h is the false prediction rate. The findings revealed that greater accuracy and sensitivity are obtained by the designed system in comparison with the results of other works in the same condition. Even though these results still are not sufficient for clinical applications. Based on the conclusions, it can generally be observed that the greater results by DTF method are in the gamma and beta frequency bands.
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Affiliation(s)
- Mona Hejazi
- 1Department of Biomedical Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
| | - Ali Motie Nasrabadi
- 2Department of Biomedical Engineering, Faculty of Biomedical Engineering, Shahed University, Tehran, Iran
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Mashour GA, Hudetz AG. Neural Correlates of Unconsciousness in Large-Scale Brain Networks. Trends Neurosci 2018; 41:150-160. [PMID: 29409683 PMCID: PMC5835202 DOI: 10.1016/j.tins.2018.01.003] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Revised: 12/12/2017] [Accepted: 01/09/2018] [Indexed: 12/21/2022]
Abstract
The biological basis of consciousness is one of the most challenging and fundamental questions in 21st century science. A related pursuit aims to identify the neural correlates and causes of unconsciousness. We review current trends in the investigation of physiological, pharmacological, and pathological states of unconsciousness at the level of large-scale functional brain networks. We focus on the roles of brain connectivity, repertoire, graph-theoretical techniques, and neural dynamics in understanding the functional brain disconnections and reduced complexity that appear to characterize these states. Persistent questions in the field, such as distinguishing true correlates, linking neural scales, and understanding differential recovery patterns, are also addressed.
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Affiliation(s)
- George A Mashour
- Neuroscience Graduate Program, Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA.
| | - Anthony G Hudetz
- Neuroscience Graduate Program, Center for Consciousness Science, Department of Anesthesiology, University of Michigan Medical School, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA
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Song Y, Zang DW, Jin YY, Wang ZJ, Ni HY, Yin JZ, Ji DX. Background rhythm frequency and theta power of quantitative EEG analysis: predictive biomarkers for cognitive impairment post-cerebral infarcts. Clin EEG Neurosci 2015; 46:142-6. [PMID: 24699438 DOI: 10.1177/1550059413517492] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2013] [Accepted: 11/08/2013] [Indexed: 11/15/2022]
Abstract
In clinical settings, cerebral infarct is a common disease of older adults, which usually increases the risk of cognitive impairment. This study aims to assess the quantitative electroencephalography (qEEG) as a predictive biomarker for the development of cognitive impairment, post-cerebral infarcts, in subjects from the Department of Neurology. They underwent biennial EEG recording. Cerebral infarct subjects, with follow-up cognitive evaluation, were analyzed for qEEG measures of background rhythm frequency (BRF) and relative δ, θ, α, and β band power. The relationship between cognitive impairment and qEEG, and other possible predictors, was assessed by Cox regression. The results showed that the risk hazard of developing cognitive impairment was 14 times higher for those with low BRF than for those with high BRF (P < .001). Hazard ratio (HR) was also significant for more than median θ band power (HR = 5, P = .002) compared with less than median θ band power. The HRs for δ, α, and β bands were equal to the baseline demographic, and clinical characteristics were not significantly different. In conclusion, qEEG measures of BRF, and relative power in θ band, are potential predictive biomarkers for cognitive impairment in patients with cerebral infarcts. These biomarkers might be valuable in early prediction of cognitive impairment in patients with cerebral infarcts.
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Affiliation(s)
- Yang Song
- Tianjin First Center Hospital, Tianjin, China
| | - Da-Wei Zang
- Tianjin First Center Hospital, Tianjin, China
| | - Yan-Yu Jin
- Tianjin First Center Hospital, Tianjin, China
| | | | - Hong-Yan Ni
- Tianjin First Center Hospital, Tianjin, China
| | | | - Dong-Xu Ji
- Tianjin First Center Hospital, Tianjin, China
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