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Staquet C, Vanhaudenhuyse A, Kandeepan S, Sanders RD, Ribeiro de Paula D, Brichant JF, Laureys S, Bonhomme V, Soddu A. Changes in Intrinsic Connectivity Networks Topology Across Levels of Dexmedetomidine-Induced Alteration of Consciousness. Anesth Analg 2024; 139:798-811. [PMID: 38289856 DOI: 10.1213/ane.0000000000006799] [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] [Indexed: 02/01/2024]
Abstract
BACKGROUND Human consciousness is generally thought to emerge from the activity of intrinsic connectivity networks (resting-state networks [RSNs]) of the brain, which have topological characteristics including, among others, graph strength and efficiency. So far, most functional brain imaging studies in anesthetized subjects have compared wakefulness and unresponsiveness, a state considered as corresponding to unconsciousness. Sedation and general anesthesia not only produce unconsciousness but also phenomenological states of preserved mental content and perception of the environment (connected consciousness), and preserved mental content but no perception of the environment (disconnected consciousness). Unresponsiveness may be seen during unconsciousness, but also during disconnectedness. Deep dexmedetomidine sedation is frequently a state of disconnected consciousness. In this study, we were interested in characterizing the RSN topology changes across 4 different and steady-state levels of dexmedetomidine-induced alteration of consciousness, namely baseline (Awake, drug-free state), Mild sedation (drowsy, still responding), Deep sedation (unresponsive), and Recovery, with a focus on changes occurring between a connected consciousness state and an unresponsiveness state. METHODS A functional magnetic resonance imaging database acquired in 14 healthy volunteers receiving dexmedetomidine sedation was analyzed using a method combining independent component analysis and graph theory, specifically looking at changes in connectivity strength and efficiency occurring during the 4 above-mentioned dexmedetomidine-induced altered consciousness states. RESULTS Dexmedetomidine sedation preserves RSN architecture. Unresponsiveness during dexmedetomidine sedation is mainly characterized by a between-networks graph strength alteration and within-network efficiency alteration of lower-order sensory RSNs, while graph strength and efficiency in higher-order RSNs are relatively preserved. CONCLUSIONS The differential dexmedetomidine-induced RSN topological changes evidenced in this study may be the signature of inadequate processing of sensory information by lower-order RSNs, and of altered communication between lower-order and higher-order networks, while the latter remain functional. If replicated in an experimental paradigm distinguishing, in unresponsive subjects, disconnected consciousness from unconsciousness, such changes would sustain the hypothesis that disconnected consciousness arises from altered information handling by lower-order sensory networks and altered communication between lower-order and higher-order networks, while the preservation of higher-order networks functioning allows for an internally generated mental content (or dream).
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Affiliation(s)
- Cecile Staquet
- From the Anesthesia and Perioperative Neuroscience Laboratory, GIGA-Consciousness, Liege University, Liege, Belgium
- Department of Anaesthetics, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
- Institute of Academic Surgery, Sydney, New South Wales, Australia
- Donders Institute for Brain Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Audrey Vanhaudenhuyse
- Interdisciplinary Center of Algology, Liege University Hospital, Liege, Belgium
- Sensation & Perception Research Group, GIGA-Consciousness, Liege University, Liege, Belgium
| | - Sivayini Kandeepan
- Department of Physics and Astronomy, Western Institute for Neuroscience, University of Western Ontario, London, Ontario, Canada
- Department of Physics, Faculty of Applied Sciences, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
| | - Robert D Sanders
- University of Sydney, Sydney, New South Wales, Australia
- Department of Anaesthetics, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
- Institute of Academic Surgery, Sydney, New South Wales, Australia
| | - Demetrius Ribeiro de Paula
- Donders Institute for Brain Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Jean François Brichant
- Department of Anesthesia and Intensive Care Medicine, Liege University Hospital, Liege, Belgium
| | - Steven Laureys
- Sensation & Perception Research Group, GIGA-Consciousness, Liege University, Liege, Belgium
- Coma Science Group, GIGA-Consciousness, Liege University, Liege, Belgium
- Centre du Cerveau , Liege University Hospital, Liege, Belgium
| | - Vincent Bonhomme
- From the Anesthesia and Perioperative Neuroscience Laboratory, GIGA-Consciousness, Liege University, Liege, Belgium
- Department of Anaesthetics, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
- Institute of Academic Surgery, Sydney, New South Wales, Australia
- Donders Institute for Brain Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Andrea Soddu
- Department of Physics and Astronomy, Western Institute for Neuroscience, University of Western Ontario, London, Ontario, Canada
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Schmierer T, Li T, Li Y. Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment. Artif Intell Med 2024; 151:102869. [PMID: 38593683 DOI: 10.1016/j.artmed.2024.102869] [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: 09/28/2023] [Revised: 01/31/2024] [Accepted: 04/03/2024] [Indexed: 04/11/2024]
Abstract
Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures. Traditional methods of depth of anaesthesia (DoA) assessment, reliant on physical characteristics, have proven inconsistent due to individual variations. In response, electroencephalography (EEG) techniques have emerged, with indices such as the Bispectral Index offering quantifiable assessments. This literature review explores the current scope and frontier of DoA research, emphasising methods utilising EEG signals for effective clinical monitoring. This review offers a critical synthesis of recent advances, specifically focusing on electroencephalography (EEG) techniques and their role in enhancing clinical monitoring. By examining 117 high-impact papers, the review delves into the nuances of feature extraction, model building, and algorithm design in EEG-based DoA analysis. Comparative assessments of these studies highlight their methodological approaches and performance, including clinical correlations with established indices like the Bispectral Index. The review identifies knowledge gaps, particularly the need for improved collaboration for data access, which is essential for developing superior machine learning models and real-time predictive algorithms for patient management. It also calls for refined model evaluation processes to ensure robustness across diverse patient demographics and anaesthetic agents. The review underscores the potential of technological advancements to enhance precision, safety, and patient outcomes in anaesthesia, paving the way for a new standard in anaesthetic care. The findings of this review contribute to the ongoing discourse on the application of EEG in anaesthesia, providing insights into the potential for technological advancement in this critical area of medical practice.
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Affiliation(s)
- Thomas Schmierer
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
| | - Tianning Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
| | - Yan Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Australia.
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Liang Z, Lan Z, Wang Y, Bai Y, He J, Wang J, Li X. The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia. J Neural Eng 2023; 20:066030. [PMID: 38055962 DOI: 10.1088/1741-2552/ad12dc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 12/06/2023] [Indexed: 12/08/2023]
Abstract
Objective.General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed.Approach.We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel-Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces.Main results.Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p< 0.001), PFC increased (p< 0.001) and PCMI decreased (p< 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p< 0.001) and nodal efficiency (NE) (p< 0.001) decreased in these two spaces. Global network indicators normalized average path length (Lave/Lr) (p< 0.01) and modularity (Q) (p< 0.05) only decreased in sensor space, while the normalized average clustering coefficient (Cave/Cr) and small-world index (σ) did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicatorsLave/LrandQwere not significantly different from the preoperative resting state in sensor space.Significance.GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.
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Affiliation(s)
- Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, People's Republic of China
| | - Zhilei Lan
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, People's Republic of China
| | - Yong Wang
- Zhuhai UM Science & Technology Research Institute, Zhuhai 519031, People's Republic of China
| | - Yang Bai
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, People's Republic of China
- Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang 330006, Jiangxi, People's Republic of China
| | - Jianghong He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Juan Wang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, People's Republic of China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, People's Republic of China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, People's Republic of China
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Li F, Li Y, Zheng H, Jiang L, Gao D, Li C, Peng Y, Cao Z, Zhang Y, Yao D, Xu T, Yuan TF, Xu P. Corrections to "Identification of the General Anesthesia Induced Loss of Consciousness by Cross Fuzzy Entropy-Based Brain Network". IEEE Trans Neural Syst Rehabil Eng 2022; 30:2970. [PMID: 37815952 DOI: 10.1109/tnsre.2022.3205527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
In the above article [1], to track the loss of consciousness (LOC) induced by general anesthesia (GA), we first developed the multi-channel cross fuzzy entropy method to construct the time- varying networks, whose temporal fluctuations were then explored and quantitatively evaluated. Since time-varying network topologies were found to fluctuate from long-range frontal-occipital to short-range prefrontal-frontal connectivity during the LOC period, a new parameter, i.e., the long-range connectivity (LRC) that measured the number of frontal-occipital connectivity, was accordingly calculated and then investigated between the coherence (COH) and cross fuzzy entropy (C-FuzzyEn) approaches, as displayed in Fig. 1. The distinct time-varying fluctuations of both approaches were indeed found within this period, where only C-FuzzyEn effectively captured the consciousness fluctuation induced by the GA.
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