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Chen S, Li B, Hu Y, Zhang Y, Dai W, Zhang X, Zhou Y, Su D. Common functional mechanisms underlying dynamic brain network changes across five general anesthetics: A rat fMRI study. CNS Neurosci Ther 2024; 30:e14866. [PMID: 39014472 PMCID: PMC11251872 DOI: 10.1111/cns.14866] [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: 04/26/2024] [Revised: 07/03/2024] [Accepted: 07/05/2024] [Indexed: 07/18/2024] Open
Abstract
BACKGROUND Reversible loss of consciousness is the primary therapeutic endpoint of general anesthesia; however, the drug-invariant mechanisms underlying anesthetic-induced unconsciousness are still unclear. This study aimed to investigate the static, dynamic, topological and organizational changes in functional brain network induced by five clinically-used general anesthetics in the rat brain. METHOD Male Sprague-Dawley rats (n = 57) were randomly allocated to received propofol, isoflurane, ketamine, dexmedetomidine, or combined isoflurane plus dexmedetomidine anesthesia. Resting-state functional magnetic resonance images were acquired under general anesthesia and analyzed for changes in dynamic functional brain networks compared to the awake state. RESULTS Different general anesthetics induced distinct patterns of functional connectivity inhibition within brain-wide networks, resulting in multi-level network reorganization primarily by impairing the functional connectivity of cortico-subcortical networks as well as by reducing information transmission capacity, intrinsic connectivity, and network architecture stability of subcortical regions. Conversely, functional connectivity and topological properties were preserved within cortico-cortical networks, albeit with fewer dynamic fluctuations under general anesthesia. CONCLUSIONS Our findings highlighted the effects of different general anesthetics on functional brain network reorganization, which might shed light on the drug-invariant mechanism of anesthetic-induced unconsciousness.
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Affiliation(s)
- Sifan Chen
- Department of Anesthesiology, Renji HospitalSchool of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
- Key Laboratory of Anesthesiology (Shanghai Jiao Tong University), Ministry of EducationShanghaiChina
- Department of RadiologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Bo Li
- Department of Anesthesiology, Renji HospitalSchool of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
- Key Laboratory of Anesthesiology (Shanghai Jiao Tong University), Ministry of EducationShanghaiChina
- Department of Radiology, Renji HospitalSchool of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Ying Hu
- Department of Radiology, Renji HospitalSchool of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yizhe Zhang
- Department of Anesthesiology, Renji HospitalSchool of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
- Key Laboratory of Anesthesiology (Shanghai Jiao Tong University), Ministry of EducationShanghaiChina
| | - Wanbing Dai
- Department of Anesthesiology, Renji HospitalSchool of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
- Key Laboratory of Anesthesiology (Shanghai Jiao Tong University), Ministry of EducationShanghaiChina
| | - Xiao Zhang
- Department of Anesthesiology, Renji HospitalSchool of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
- Key Laboratory of Anesthesiology (Shanghai Jiao Tong University), Ministry of EducationShanghaiChina
| | - Yan Zhou
- Department of Radiology, Renji HospitalSchool of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
| | - Diansan Su
- Department of Anesthesiology, Renji HospitalSchool of Medicine, Shanghai Jiao Tong UniversityShanghaiChina
- Key Laboratory of Anesthesiology (Shanghai Jiao Tong University), Ministry of EducationShanghaiChina
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Liang Z, Chang Y, Liu X, Cao S, Chen Y, Wang T, Xu J, Li D, Zhang J. Changes in information integration and brain networks during propofol-, dexmedetomidine-, and ketamine-induced unresponsiveness. Br J Anaesth 2024; 132:528-540. [PMID: 38105166 DOI: 10.1016/j.bja.2023.11.033] [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: 09/23/2022] [Revised: 10/18/2023] [Accepted: 11/07/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Information integration and network science are important theories for quantifying consciousness. However, whether these theories propose drug- or conscious state-related changes in EEG during anaesthesia-induced unresponsiveness remains unknown. METHODS A total of 72 participants were randomised to receive i.v. infusion of propofol, dexmedetomidine, or ketamine at a constant infusion rate until loss of responsiveness. High-density EEG was recorded during the consciousness transition from the eye-closed baseline to the unresponsiveness state and then to the recovery of the responsiveness state. Permutation cross mutual information (PCMI) and PCMI-based brain networks in broadband (0.1-45 Hz) and sub-band frequencies were used to analyse drug- and state-related EEG signature changes. RESULTS PCMI and brain networks exhibited state-related changes in certain brain regions and frequency bands. The within-area PCMI of the frontal, parietal, and occipital regions, and the between-area PCMI of the parietal-occipital region (median [inter-quartile ranges]), baseline vs unresponsive were as follows: 0.54 (0.46-0.58) vs 0.46 (0.40-0.50), 0.58 (0.52-0.60) vs 0.48 (0.44-0.53), 0.54 (0.49-0.59) vs 0.47 (0.42-0.52) decreased during anaesthesia for three drugs (P<0.05). Alpha PCMI in the frontal region, and gamma PCMI in the posterior area significantly decreased in the unresponsive state (P<0.05). The frontal, parietal, and occipital nodal clustering coefficients and parietal nodal efficiency decreased in the unresponsive state (P<0.05). The increased normalised path length in delta, theta, and gamma bands indicated impaired global integration (P<0.05). CONCLUSIONS The three anaesthetics caused changes in information integration patterns and network functions. Thus, it is possible to build a quantifying framework for anaesthesia-induced conscious state changes on the EEG scale using PCMI and network science.
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Affiliation(s)
- Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, P.R. China; Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao, P.R. China
| | - Yu Chang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, P.R. China; Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao, P.R. China
| | - Xiaoge Liu
- Department of Anaesthesiology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P.R. China
| | - Shumei Cao
- Department of Anaesthesiology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P.R. China
| | - Yali Chen
- Department of Anaesthesiology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P.R. China
| | - Tingting Wang
- Department of Anaesthesiology, Huashan Hospital, Fudan University, Shanghai, P.R. China
| | - Jianghui Xu
- Department of Anaesthesiology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P.R. China
| | - Duan Li
- Center for Consciousness Science, Department of Anaesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jun Zhang
- Department of Anaesthesiology, Fudan University Shanghai Cancer Center, Shanghai, P.R. China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P.R. China.
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Zanner R, Berger S, Schröder N, Kreuzer M, Schneider G. Separation of responsive and unresponsive patients under clinical conditions: comparison of symbolic transfer entropy and permutation entropy. J Clin Monit Comput 2024; 38:187-196. [PMID: 37436600 PMCID: PMC10879366 DOI: 10.1007/s10877-023-01046-w] [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: 11/08/2022] [Accepted: 06/13/2023] [Indexed: 07/13/2023]
Abstract
Electroencephalogram (EEG)-based monitoring during general anesthesia may help prevent harmful effects of high or low doses of general anesthetics. There is currently no convincing evidence in this regard for the proprietary algorithms of commercially available monitors. The purpose of this study was to investigate whether a more mechanism-based parameter of EEG analysis (symbolic transfer entropy, STE) can separate responsive from unresponsive patients better than a strictly probabilistic parameter (permutation entropy, PE) under clinical conditions. In this prospective single-center study, the EEG of 60 surgical ASA I-III patients was recorded perioperatively. During induction of and emergence from anesthesia, patients were asked to squeeze the investigators' hand every 15s. Time of loss of responsiveness (LoR) during induction and return of responsiveness (RoR) during emergence from anesthesia were registered. PE and STE were calculated at -15s and +30s of LoR and RoR and their ability to separate responsive from unresponsive patients was evaluated using accuracy statistics. 56 patients were included in the final analysis. STE and PE values decreased during anesthesia induction and increased during emergence. Intra-individual consistency was higher during induction than during emergence. Accuracy values during LoR and RoR were 0.71 (0.62-0.79) and 0.60 (0.51-0.69), respectively for STE and 0.74 (0.66-0.82) and 0.62 (0.53-0.71), respectively for PE. For the combination of LoR and RoR, values were 0.65 (0.59-0.71) for STE and 0.68 (0.62-0.74) for PE. The ability to differentiate between the clinical status of (un)responsiveness did not significantly differ between STE and PE at any time. Mechanism-based EEG analysis did not improve differentiation of responsive from unresponsive patients compared to the probabilistic PE.Trial registration: German Clinical Trials Register ID: DRKS00030562, November 4, 2022, retrospectively registered.
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Affiliation(s)
- Robert Zanner
- Department of Anesthesiology, HELIOS University Clinic Wuppertal, Witten/Herdecke University, Heusnerstr. 40, 42283, Wuppertal, Germany
- Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Sebastian Berger
- Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Natalie Schröder
- Department of Anesthesiology, HELIOS University Clinic Wuppertal, Witten/Herdecke University, Heusnerstr. 40, 42283, Wuppertal, Germany
- Klinikum Fünfseenland, Robert-Koch-Allee 6, 82131, Gauting, Germany
| | - Matthias Kreuzer
- Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Gerhard Schneider
- Department of Anesthesiology, HELIOS University Clinic Wuppertal, Witten/Herdecke University, Heusnerstr. 40, 42283, Wuppertal, Germany.
- Department of Anesthesiology and Intensive Care, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
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Tauber JM, Brincat SL, Stephen EP, Donoghue JA, Kozachkov L, Brown EN, Miller EK. Propofol-mediated Unconsciousness Disrupts Progression of Sensory Signals through the Cortical Hierarchy. J Cogn Neurosci 2024; 36:394-413. [PMID: 37902596 PMCID: PMC11161138 DOI: 10.1162/jocn_a_02081] [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: 10/31/2023]
Abstract
A critical component of anesthesia is the loss of sensory perception. Propofol is the most widely used drug for general anesthesia, but the neural mechanisms of how and when it disrupts sensory processing are not fully understood. We analyzed local field potential and spiking recorded from Utah arrays in auditory cortex, associative cortex, and cognitive cortex of nonhuman primates before and during propofol-mediated unconsciousness. Sensory stimuli elicited robust and decodable stimulus responses and triggered periods of stimulus-related synchronization between brain areas in the local field potential of Awake animals. By contrast, propofol-mediated unconsciousness eliminated stimulus-related synchrony and drastically weakened stimulus responses and information in all brain areas except for auditory cortex, where responses and information persisted. However, we found stimuli occurring during spiking Up states triggered weaker spiking responses than in Awake animals in auditory cortex, and little or no spiking responses in higher order areas. These results suggest that propofol's effect on sensory processing is not just because of asynchronous Down states. Rather, both Down states and Up states reflect disrupted dynamics.
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Affiliation(s)
- John M Tauber
- Massachusetts Institute of Technology, Cambridge, MA
| | | | | | | | - Leo Kozachkov
- Massachusetts Institute of Technology, Cambridge, MA
| | - Emery N Brown
- Massachusetts Institute of Technology, Cambridge, MA
- Massachusetts General Hospital, Boston
- Harvard University, Cambridge, MA
| | - Earl K Miller
- Massachusetts Institute of Technology, Cambridge, MA
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Jiang X, Wen X, Ou G, Li S, Chen Y, Zhang J, Liang Z. Propofol modulates neural dynamics of thalamo-cortical system associated with anesthetic levels in rats. Cogn Neurodyn 2023; 17:1541-1559. [PMID: 37974577 PMCID: PMC10640503 DOI: 10.1007/s11571-022-09912-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 10/14/2022] [Accepted: 10/28/2022] [Indexed: 11/24/2022] Open
Abstract
The thalamocortical system plays an important role in consciousness. How anesthesia modulates the thalamocortical interactions is not completely known. We simultaneously recorded local field potentials(LFPs) in thalamic reticular nucleus(TRN) and ventroposteromedial thalamic nucleus(VPM), and electrocorticographic(ECoG) activities in frontal and occipital cortices in freely moving rats (n = 11). We analyzed the changes in thalamic and cortical local spectral power and connectivities, which were measured with phase-amplitude coupling (PAC), coherence and multivariate Granger causality, at the states of baseline, intravenous infusion of propofol 20, 40, 80 mg/kg/h and after recovery of righting reflex. We found that propofol-induced burst-suppression results in a synchronous decrease of spectral power in thalamus and cortex (p < 0.001 for all frequency bands). The cross-frequency PAC increased by propofol, characterized by gradually stronger 'trough-max' pattern in TRN and stronger 'peak-max' pattern in cortex. The cross-region PAC increased in the phase of TRN modulating the amplitude of cortex. The functional connectivity (FC) between TRN and cortex for α/β bands also significantly increased (p < 0.040), with increased directional connectivity from TRN to cortex under propofol anesthesia. In contrast, the corticocortical FC significantly decreased (p < 0.047), with decreased directional connectivity from frontal cortex to occipital cortex. However, the thalamothalamic functional and directional connectivities remained largely unchanged by propofol anesthesia. The spectral powers and connectivities are differentially modulated with the changes of propofol doses, suggesting the changes in neural dynamics in thalamocortical system could be used for distinguishing different vigilance levels caused by propofol. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09912-0.
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Affiliation(s)
- Xuliang Jiang
- Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032 People’s Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 People’s Republic of China
| | - Xin Wen
- 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
| | - Guoyao Ou
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, 200040 People’s Republic of China
| | - Shitong Li
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, 200040 People’s Republic of China
| | - Yali Chen
- Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032 People’s Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 People’s Republic of China
| | - Jun Zhang
- Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, 200032 People’s Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032 People’s Republic of China
| | - 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
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Zelmann R, Paulk AC, Tian F, Balanza Villegas GA, Dezha Peralta J, Crocker B, Cosgrove GR, Richardson RM, Williams ZM, Dougherty DD, Purdon PL, Cash SS. Differential cortical network engagement during states of un/consciousness in humans. Neuron 2023; 111:3479-3495.e6. [PMID: 37659409 PMCID: PMC10843836 DOI: 10.1016/j.neuron.2023.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 06/13/2023] [Accepted: 08/08/2023] [Indexed: 09/04/2023]
Abstract
What happens in the human brain when we are unconscious? Despite substantial work, we are still unsure which brain regions are involved and how they are impacted when consciousness is disrupted. Using intracranial recordings and direct electrical stimulation, we mapped global, network, and regional involvement during wake vs. arousable unconsciousness (sleep) vs. non-arousable unconsciousness (propofol-induced general anesthesia). Information integration and complex processing we`re reduced, while variability increased in any type of unconscious state. These changes were more pronounced during anesthesia than sleep and involved different cortical engagement. During sleep, changes were mostly uniformly distributed across the brain, whereas during anesthesia, the prefrontal cortex was the most disrupted, suggesting that the lack of arousability during anesthesia results not from just altered overall physiology but from a disconnection between the prefrontal and other brain areas. These findings provide direct evidence for different neural dynamics during loss of consciousness compared with loss of arousability.
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Affiliation(s)
- Rina Zelmann
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA.
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
| | - Fangyun Tian
- Department of Anesthesia, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick L Purdon
- Department of Anesthesia, Massachusetts General Hospital, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
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7
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Wodeyar A, Marshall FA, Chu CJ, Eden UT, Kramer MA. Different Methods to Estimate the Phase of Neural Rhythms Agree But Only During Times of Low Uncertainty. eNeuro 2023; 10:ENEURO.0507-22.2023. [PMID: 37833061 PMCID: PMC10626504 DOI: 10.1523/eneuro.0507-22.2023] [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: 12/16/2022] [Revised: 08/25/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023] Open
Abstract
Rhythms are a common feature of brain activity. Across different types of rhythms, the phase has been proposed to have functional consequences, thus requiring its accurate specification from noisy data. Phase is conventionally specified using techniques that presume a frequency band-limited rhythm. However, in practice, observed brain rhythms are typically nonsinusoidal and amplitude modulated. How these features impact methods to estimate phase remains unclear. To address this, we consider three phase estimation methods, each with different underlying assumptions about the rhythm. We apply these methods to rhythms simulated with different generative mechanisms and demonstrate inconsistency in phase estimates across the different methods. We propose two improvements to the practice of phase estimation: (1) estimating confidence in the phase estimate, and (2) examining the consistency of phase estimates between two (or more) methods.
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Affiliation(s)
- Anirudh Wodeyar
- Department of Mathematics & Statistics, Boston University, Boston, MA 02215
| | | | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02215
- Harvard Medical School, Boston, MA 02114
| | - Uri T Eden
- Department of Mathematics & Statistics, Boston University, Boston, MA 02215
- Center for Systems Neuroscience, Boston University, Boston, MA 02215
| | - Mark A Kramer
- Department of Mathematics & Statistics, Boston University, Boston, MA 02215
- Center for Systems Neuroscience, Boston University, Boston, MA 02215
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8
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Jin X, Liang Z, Wen X, Wang Y, Bai Y, Xia X, He J, Sleigh J, Li X. The Characteristics of Electroencephalogram Signatures in Minimally Conscious State Patients Induced by General Anesthesia. IEEE Trans Biomed Eng 2023; 70:3239-3247. [PMID: 37335799 DOI: 10.1109/tbme.2023.3287203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Abstract
OBJECTIVE General anesthesia (GA) is necessary for surgery, even for patients in a minimally conscious state (MCS). The characteristics of the electroencephalogram (EEG) signatures of the MCS patients under GA are still unclear. METHODS The EEG during GA were recorded from 10 MCS patients undergoing spinal cord stimulation surgery. The power spectrum, phase-amplitude coupling (PAC), the diversity of connectivity, and the functional network were investigated. Long term recovery was assessed by the Coma Recovery Scale-Revised at one year after the surgery, and the characteristics of the patients with good or bad prognosis status were compared. RESULTS For the four MCS patients with good prognostic recovery, slow oscillation (0.1-1 Hz) and the alpha band (8-12 Hz) in the frontal areas increased during the maintenance of a surgical state of anesthesia (MOSSA), and "peak-max" and "trough-max" patterns emerged in frontal and parietal areas. During MOSSA, the six MCS patients with bad prognosis demonstrated: increased modulation index, reduced diversity of connectivity (from mean±SD of 0.877 ± 0.003 to 0.776 ± 0.003, p < 0.001), reduced function connectivity significantly in theta band (from mean±SD of 1.032 ± 0.043 to 0.589 ± 0.036, p < 0.001, in prefrontal-frontal; and from mean±SD of 0.989 ± 0.043 to 0.684 ± 0.036, p < 0.001, in frontal-parietal) and reduced local and global efficiency of the network in delta band. CONCLUSIONS A bad prognosis in MCS patients is associated with signs of impaired thalamocortical and cortico-cortical connectivity - as indicated by inability to produce inter-frequency coupling and phase synchronization. These indices may have a role in predicting the long-term recovery of MCS patients.
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9
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Wodeyar A, Marshall FA, Chu CJ, Eden UT, Kramer MA. Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522914. [PMID: 37693592 PMCID: PMC10491120 DOI: 10.1101/2023.01.05.522914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Rhythms are a common feature of brain activity. Across different types of rhythms, the phase has been proposed to have functional consequences, thus requiring its accurate specification from noisy data. Phase is conventionally specified using techniques that presume a frequency band-limited rhythm. However, in practice, observed brain rhythms are typically non-sinusoidal and amplitude modulated. How these features impact methods to estimate phase remains unclear. To address this, we consider three phase estimation methods, each with different underlying assumptions about the rhythm. We apply these methods to rhythms simulated with different generative mechanisms and demonstrate inconsistency in phase estimates across the different methods. We propose two improvements to the practice of phase estimation: (1) estimating confidence in the phase estimate, and (2) examining the consistency of phase estimates between two (or more) methods.
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Affiliation(s)
- Anirudh Wodeyar
- Department of Mathematics & Statistics, Boston University, Boston MA, USA, 02215
| | - François A. Marshall
- Department of Mathematics & Statistics, Boston University, Boston MA, USA, 02215
| | - Catherine J. Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA; USA, 02215
- Harvard Medical School, Boston, MA, USA, 02114
| | - Uri T. Eden
- Department of Mathematics & Statistics, Boston University, Boston MA, USA, 02215
- Center for Systems Neuroscience, Boston University, Boston MA, USA, 02215
| | - Mark A. Kramer
- Department of Mathematics & Statistics, Boston University, Boston MA, USA, 02215
- Center for Systems Neuroscience, Boston University, Boston MA, USA, 02215
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10
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Wang H, Zhang Y, Cheng H, Yan F, Song D, Wang Q, Cai S, Wang Y, Huang L. Selective corticocortical connectivity suppression during propofol-induced anesthesia in healthy volunteers. Cogn Neurodyn 2022; 16:1029-1043. [PMID: 36237410 PMCID: PMC9508318 DOI: 10.1007/s11571-021-09775-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 11/17/2021] [Accepted: 12/13/2021] [Indexed: 11/03/2022] Open
Abstract
We comprehensively studied directional feedback and feedforward connectivity to explore potential connectivity changes that underlie propofol-induced deep sedation. We further investigated the corticocortical connectivity patterns within and between hemispheres. Sixty-channel electroencephalographic data were collected from 19 healthy volunteers in a resting wakefulness state and propofol-induced deep unconsciousness state defined by a bispectral index value of 40. A source analysis was employed to locate cortical activity. The Desikan-Killiany atlas was used to partition cortices, and directional functional connectivity was assessed by normalized symbolic transfer entropy between higher-order (prefrontal and frontal) and lower-order (auditory, sensorimotor and visual) cortices and between hot-spot frontal and parietal cortices. We found that propofol significantly suppressed feedforward connectivity from the left parietal to right frontal cortex and bidirectional connectivity between the left frontal and left parietal cortex, between the frontal and auditory cortex, and between the frontal and sensorimotor cortex. However, there were no significant changes in either feedforward or feedback connectivity between the prefrontal and all the lower-order cortices and between the frontal and visual cortices or in feedback connectivity from the frontal to parietal cortex. Propofol anesthetic selectively decreased the unidirectional interaction between higher-order frontoparietal cortices and bidirectional interactions between the higher-order frontal cortex and lower-order auditory and sensorimotor cortices, which indicated that both feedback and feedforward connectivity were suppressed under propofol-induced deep sedation. Our findings provide critical insights into the connectivity changes underlying the top-down mechanism of propofol anesthesia at deep sedation. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-021-09775-x.
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Affiliation(s)
- Haidong Wang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Yun Zhang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Huanhuan Cheng
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Fei Yan
- Department of Anesthesiology & Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, 710061 China
| | - Dawei Song
- Department of Anesthesiology & Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, 710061 China
| | - Qiang Wang
- Department of Anesthesiology & Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, 710061 China
| | - Suping Cai
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Yubo Wang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
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Dynamic alpha-gamma phase-amplitude coupling signatures during sevoflurane-induced loss and recovery of consciousness. Neurosci Res 2022; 185:20-28. [PMID: 36084701 DOI: 10.1016/j.neures.2022.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 09/01/2022] [Accepted: 09/04/2022] [Indexed: 11/20/2022]
Abstract
Phase-amplitude coupling (PAC) plays an important role in anesthetic-induced unconsciousness. The delta-alpha PAC signature during anesthetic-induced unconsciousness is gradually becoming known; however, the frequency dependence and spatial characteristics of PAC are still unclear. Multi-channel electroencephalography (EEG) was performed during the loss and recovery phases of consciousness in patients undergoing general anesthesia using sevoflurane. First, a spectral analysis was used to investigate the power change of the different frequency bands in the EEG signals. Second, PAC comodulogram analysis was performed to confirm the frequencies of the PAC phase drivers. Finally, to investigate the spatial characteristics of PAC, a novel PAC network was constructed using within- and cross-lead PAC, and a K-means clustering algorithm was used to identify PAC network patterns. Our results show that, in addition to the delta-alpha PAC, unconsciousness induced by sevoflurane was accompanied by spatial non-uniform alpha-gamma PAC in the cortical network, and dynamic PAC patterns between the anterior and posterior brain were observed during the unconscious phase. The dynamic transition of PAC network patterns indicates that brain states under sevoflurane-induced unconsciousness emerge from the regulation of functional integration and segregation instantiated by delta-alpha and alpha-gamma PAC.
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12
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Chen K, Xie T, Ma L, Hudson AE, Ai Q, Liu Q. A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2077-2087. [PMID: 35862321 DOI: 10.1109/tnsre.2022.3193103] [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: 11/10/2022]
Abstract
Investigating neural mechanisms of anesthesia process and developing efficient anesthetized state detection methods are especially on high demand for clinical consciousness monitoring. Traditional anesthesia monitoring methods are not involved with the topological changes between electrodes covering the prefrontal-parietal cortices, by investigating electrocorticography (ECoG). To fill this gap, a framework based on the two-stream graph convolutional network (GCN) was proposed, i.e., one stream for extracting topological structure features, and the other one for extracting node features. The two-stream graph convolutional network includes GCN Model 1 and GCN Model 2. For GCN Model 1, brain connectivity networks were constructed by using phase lag index (PLI), representing different structure features. A common adjacency matrix was founded through the dual-graph method, the structure features were expressed on nodes. Therefore, the traditional spectral graph convolutional network can be directly applied on the graphs with changing topological structures. On the other hand, the average of the absolute signal amplitudes was calculated as node features, then a fully connected matrix was constructed as the adjacency matrix of these node features, as the input of GCN Model 2. This method learns features of both topological structure and nodes of the graph, and uses a dual-graph approach to enhance the focus on topological structure features. Based on the ECoG signals of monkeys, results show that this method which can distinguish awake state, moderate sedation and deep sedation achieved an accuracy of 92.75% in group-level experiments and mean accuracy of 93.50% in subject-level experiments. Our work verifies the excellence of the graph convolutional network in anesthesia monitoring, the high recognition accuracy also shows that the brain network may carry neurological markers associated with anesthesia.
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Abstract
Background: The wakeful brain can easily access and coordinate a large repertoire of different states—dynamics suggestive of “criticality.” Anesthesia causes loss of criticality at the level of electroencephalogram waveforms, but the criticality of brain network connectivity is less well studied. The authors hypothesized that propofol anesthesia is associated with abrupt and divergent changes in brain network connectivity for different frequencies and time scales—characteristic of a phase transition, a signature of loss of criticality. Methods: As part of a previously reported study, 16 volunteers were given propofol in slowly increasing brain concentrations, and their behavioral responsiveness was assessed. The network dynamics from 31-channel electroencephalogram data were calculated from 1 to 20 Hz using four phase and envelope amplitude–based functional connectivity metrics that covered a wide range of time scales from milliseconds to minutes. The authors calculated network global efficiency, clustering coefficient, and statistical complexity (using the Jensen–Shannon divergence) for each functional connectivity metric and compared their findings with those from an in silico Kuramoto network model. Results: The transition to anesthesia was associated with critical slowing and then abrupt profound decreases in global network efficiency of 2 Hz power envelope metrics (from mean ± SD of 0.64 ± 0.15 to 0.29 ± 0.28 absolute value, P < 0.001, for medium; and from 0.47 ± 0.13 to 0.24 ± 0.21, P < 0.001, for long time scales) but with an increase in global network efficiency for 10 Hz weighted phase lag index (from 0.30 ± 0.20 to 0.72 ± 0.06, P < 0.001). Network complexity decreased for both the 10 Hz hypersynchronous (0.44 ± 0.13 to 0.23 ± 0.08, P < 0.001), and the 2 Hz asynchronous (0.73 ± 0.08 to 0.40 ± 0.13, P < 0.001) network states. These patterns of network coupling were consistent with those of the Kuramoto model of an order–disorder phase transition. Conclusions: Around loss of behavioral responsiveness, a small increase in propofol concentrations caused a collapse of long time scale power envelope connectivity and an increase in 10 Hz phase-based connectivity—suggestive of a brain network phase transition. Temporospatial electroencephalographic analysis of brain network dynamics over a wide range of frequencies and time scales in 16 volunteers receiving slowly increasing concentrations of propofol revealed that transition to unresponsiveness was associated with a sudden rise in alpha frequency network phase synchrony anteriorly, but also a transient surge and then loss of network coupling over long (tens of seconds) time scales. Deep anesthesia was characterized by alpha waveform hypersynchrony and slow-wave power envelope dissynchrony across the whole cortex. These observations suggest that propofol anesthesia is associated with a constellation of changes in network connectivity across frequencies and time scales that are signatures of sharp and sudden transitions in the behavior of networks.
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Xu R, Bichot NP, Takahashi A, Desimone R. The cortical connectome of primate lateral prefrontal cortex. Neuron 2022; 110:312-327.e7. [PMID: 34739817 PMCID: PMC8776613 DOI: 10.1016/j.neuron.2021.10.018] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/09/2021] [Accepted: 10/11/2021] [Indexed: 01/21/2023]
Abstract
The lateral prefrontal cortex (LPFC) of primates plays an important role in executive control, but how it interacts with the rest of the cortex remains unclear. To address this, we densely mapped the cortical connectome of LPFC, using electrical microstimulation combined with functional MRI (EM-fMRI). We found isomorphic mappings between LPFC and five major processing domains composing most of the cerebral cortex except early sensory and motor areas. An LPFC grid of ∼200 stimulation sites topographically mapped to separate grids of activation sites in the five domains, coarsely resembling how the visual cortex maps the retina. The temporal and parietal maps largely overlapped in LPFC, suggesting topographically organized convergence of the ventral and dorsal streams, and the other maps overlapped at least partially. Thus, the LPFC contains overlapping, millimeter-scale maps that mirror the organization of major cortical processing domains, supporting LPFC's role in coordinating activity within and across these domains.
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Affiliation(s)
- Rui Xu
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Narcisse P Bichot
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Atsushi Takahashi
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Robert Desimone
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
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15
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Giacometti C, Dureux A, Autran-Clavagnier D, Wilson CRE, Sallet J, Dirheimer M, Procyk E, Hadj-Bouziane F, Amiez C. Frontal Cortical Functional Connectivity Is Impacted by Anaesthesia in Macaques. Cereb Cortex 2021; 32:4050-4067. [PMID: 34974618 DOI: 10.1093/cercor/bhab465] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 10/19/2021] [Accepted: 11/09/2021] [Indexed: 01/02/2023] Open
Abstract
A critical aspect of neuroscience is to establish whether and how brain networks evolved across primates. To date, most comparative studies have used resting-state functional magnetic resonance imaging (rs-fMRI) in anaesthetized nonhuman primates and in awake humans. However, anaesthesia strongly affects rs-fMRI signals. The present study investigated the impact of the awareness state (anaesthesia vs. awake) within the same group of macaque monkeys on the rs-fMRI functional connectivity organization of a well-characterized network in the human brain, the cingulo-frontal lateral network. Results in awake macaques show that rostral seeds in the cingulate sulcus exhibited stronger correlation strength with rostral compared to caudal lateral frontal cortical areas, while more caudal seeds displayed stronger correlation strength with caudal compared to anterior lateral frontal cortical areas. Critically, this inverse rostro-caudal functional gradient was abolished under anaesthesia. This study demonstrated a similar functional connectivity (FC) organization of the cingulo-frontal cortical network in awake macaque to that previously uncovered in the human brain pointing toward a preserved FC organization from macaque to human. However, it can only be observed in awake state suggesting that this network is sensitive to anaesthesia and warranting significant caution when comparing FC patterns across species under different states.
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Affiliation(s)
- Camille Giacometti
- Univ Lyon, Université Claude Bernard Lyon 1, Institut National de la Santé Et de la Recherche Médicale, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Audrey Dureux
- Integrative Multisensory Perception Action & Cognition Team (ImpAct), INSERM U1028, CNRS UMR5292, Lyon Neuroscience Research Center (CRNL), Lyon, France, University of Lyon 1, Lyon, France
| | - Delphine Autran-Clavagnier
- Univ Lyon, Université Claude Bernard Lyon 1, Institut National de la Santé Et de la Recherche Médicale, Stem Cell and Brain Research Institute U1208, Bron, France.,Inovarion, 75005 Paris, France
| | - Charles R E Wilson
- Univ Lyon, Université Claude Bernard Lyon 1, Institut National de la Santé Et de la Recherche Médicale, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Jérôme Sallet
- Univ Lyon, Université Claude Bernard Lyon 1, Institut National de la Santé Et de la Recherche Médicale, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Manon Dirheimer
- Integrative Multisensory Perception Action & Cognition Team (ImpAct), INSERM U1028, CNRS UMR5292, Lyon Neuroscience Research Center (CRNL), Lyon, France, University of Lyon 1, Lyon, France
| | - Emmanuel Procyk
- Univ Lyon, Université Claude Bernard Lyon 1, Institut National de la Santé Et de la Recherche Médicale, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Fadila Hadj-Bouziane
- Integrative Multisensory Perception Action & Cognition Team (ImpAct), INSERM U1028, CNRS UMR5292, Lyon Neuroscience Research Center (CRNL), Lyon, France, University of Lyon 1, Lyon, France
| | - Céline Amiez
- Univ Lyon, Université Claude Bernard Lyon 1, Institut National de la Santé Et de la Recherche Médicale, Stem Cell and Brain Research Institute U1208, Bron, France
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Xie T, Chen K, Ma L, Ai Q, Liu Q, Hudson AE. Brain Connectivity Analysis in Anesthetized and Awake States: an ECoG Study in Monkeys. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:117-120. [PMID: 34891252 DOI: 10.1109/embc46164.2021.9631095] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Increasingly, studies have shown that changes in brain network topology accompany loss of consciousness such that the functional connectivity of the prefrontal-parietal network differs significantly in anesthetized and awake states. In this work, anesthetized and awake segments of electrocorticography were selected from two monkeys. Using phase lag index, functional connectivity matrices were built in multiple frequency bands. Quantifying topological changes in brain network through graph-theoretic properties revealed significant differences between the awake and anesthetized states. Compared to the awake state, there were distinct increases in overall and Delta prefrontal-frontal connectivity, and decreases in Alpha, Beta1 and Beta2 prefrontal-frontal connectivity during the anesthetized state, which indicate a change in the topology of the small-world network. Using functional connectivity features we achieved a satisfactory classification accuracy (93.68%). Our study demonstrates that functional connectivity features are of sufficient power to distinguish awake versus anesthetized state.Clinical Relevance- This explores the brain network topology in awake and anesthetized states, and provides new ideas for clinical depth of anesthesia monitoring.
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17
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Liang Z, Jin X, Ren Y, Yu T, Li X. Propofol Anesthesia Decreased the Efficiency of Long-Range Cortical Interaction in Humans. IEEE Trans Biomed Eng 2021; 69:165-175. [PMID: 34161232 DOI: 10.1109/tbme.2021.3090027] [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: 11/09/2022]
Abstract
OBJECTIVE Phase-amplitude coupling (PAC) has recently been used to illuminate cross-frequency coordination in neurophysiological activity of electroencephalogram. However, the PAC at a meso-scale (electrocorticogram, ECoG) and PAC between different areas have still not been fully clarified. METHODS In this study, we analyzed ECoG data recorded from surgical patients (n = 9) with pharmaco-resistant epilepsy during the surgical treatment. The modulogram and a genuine modulation index, based on a Kullback-Leibler distance and permutation test method, were developed and used to measure the slow oscillation (SO) (0.15-1 Hz)-α (8-13 Hz) PAC of within-lead and cross-lead during transitions from states of wakefulness to unconsciousness during propofol induced general anesthesia. RESULTS In within-lead SO-α PAC, the modulation index increased in the unconscious state (p < 0.05, Tukey's test), the percentages of genuine modulation indices also increased in the unconscious state (p < 0.001 in the frontal area and p < 0.01 in the parietal area), and distinct PAC patterns emerged more often. In cross-lead SO-α PAC, there are fewer PAC patterns compared to within-lead, and the percentages of genuine modulation indices decreased significantly (p < 0.001). CONCLUSION The increased modulation index of within-lead and cross-lead SO-α PAC is associated with a reduction of information integration and the efficiency of long distance synchronization. These findings demonstrate that the propofol causes the neuronal populations to enter a 'busy' state in a local scale, which prevents the information integration in long-range areas.
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Varley TF, Denny V, Sporns O, Patania A. Topological analysis of differential effects of ketamine and propofol anaesthesia on brain dynamics. ROYAL SOCIETY OPEN SCIENCE 2021; 8:201971. [PMID: 34168888 PMCID: PMC8220281 DOI: 10.1098/rsos.201971] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 05/21/2021] [Indexed: 05/07/2023]
Abstract
Research has found that the vividness of conscious experience is related to brain dynamics. Despite both being anaesthetics, propofol and ketamine produce different subjective states: we explore the different effects of these two anaesthetics on the structure of dynamic attractors reconstructed from electrophysiological activity recorded from cerebral cortex of two macaques. We used two methods: the first embeds the recordings in a continuous high-dimensional manifold on which we use topological data analysis to infer the presence of higher-order dynamics. The second reconstruction, an ordinal partition network embedding, allows us to create a discrete state-transition network, which is amenable to information-theoretic analysis and contains rich information about state-transition dynamics. We find that the awake condition generally had the 'richest' structure, visiting the most states, the presence of pronounced higher-order structures, and the least deterministic dynamics. By contrast, the propofol condition had the most dissimilar dynamics, transitioning to a more impoverished, constrained, low-structure regime. The ketamine condition, interestingly, seemed to combine aspects of both: while it was generally less complex than the awake condition, it remained well above propofol in almost all measures. These results provide deeper and more comprehensive insights than what is typically gained by using point-measures of complexity.
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Affiliation(s)
- Thomas F. Varley
- Psychological & Brain Sciences, Indiana University, Bloomington, IN 47401, USA
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47401, USA
| | - Vanessa Denny
- Psychological & Brain Sciences, Indiana University, Bloomington, IN 47401, USA
| | - Olaf Sporns
- Psychological & Brain Sciences, Indiana University, Bloomington, IN 47401, USA
- Indiana University Network Sciences Institute (IUNI), Bloomington, IN 47401, USA
| | - Alice Patania
- Indiana University Network Sciences Institute (IUNI), Bloomington, IN 47401, USA
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19
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Zhao X, Wang Y, Zhang Y, Wang H, Ren J, Yan F, Song D, Du R, Wang Q, Huang L. Propofol-Induced Anesthesia Alters Corticocortical Functional Connectivity in the Human Brain: An EEG Source Space Analysis. Neurosci Bull 2021; 37:563-568. [PMID: 33687648 DOI: 10.1007/s12264-021-00633-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 08/24/2020] [Indexed: 12/19/2022] Open
Affiliation(s)
- Xue Zhao
- School of Life Science and Technology, Xidian University, Xi'an, 710071, China
| | - Yubo Wang
- School of Life Science and Technology, Xidian University, Xi'an, 710071, China
| | - Yun Zhang
- School of Life Science and Technology, Xidian University, Xi'an, 710071, China
| | - Haidong Wang
- School of Life Science and Technology, Xidian University, Xi'an, 710071, China
| | - Junchan Ren
- School of Life Science and Technology, Xidian University, Xi'an, 710071, China
| | - Fei Yan
- Department of Anesthesiology and Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Dawei Song
- Department of Anesthesiology and Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Ruini Du
- Department of Anesthesiology and Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Qiang Wang
- Department of Anesthesiology and Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, Xi'an, 710071, China.
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20
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Desflurane Anesthesia Alters Cortical Layer-specific Hierarchical Interactions in Rat Cerebral Cortex. Anesthesiology 2020; 132:1080-1090. [PMID: 32101967 DOI: 10.1097/aln.0000000000003179] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Neurocognitive investigations suggest that conscious sensory perception depends on recurrent neuronal interactions among sensory, parietal, and frontal cortical regions, which are suppressed by general anesthetics. The purpose of this work was to investigate if local interactions in sensory cortex are also altered by anesthetics. The authors hypothesized that desflurane would reduce recurrent neuronal interactions in cortical layer-specific manner consistent with the anatomical disposition of feedforward and feedback pathways. METHODS Single-unit neuronal activity was measured in freely moving adult male rats (268 units; 10 animals) using microelectrode arrays chronically implanted in primary and secondary visual cortex. Layer-specific directional interactions were estimated by mutual information and transfer entropy of multineuron spike patterns within and between cortical layers three and five. The effect of incrementally increasing and decreasing steady-state concentrations of desflurane (0 to 8% to 0%) was tested for statistically significant quadratic trend across the successive anesthetic states. RESULTS Desflurane produced robust, state-dependent reduction (P = 0.001) of neuronal interactions between primary and secondary visual areas and between layers three and five, as indicated by mutual information (37 and 41% decrease at 8% desflurane from wakeful baseline at [mean ± SD] 0.52 ± 0.51 and 0.53 ± 0.51 a.u., respectively) and transfer entropy (77 and 78% decrease at 8% desflurane from wakeful baseline at 1.86 ± 1.56 a.u. and 1.87 ± 1.67 a.u., respectively). In addition, a preferential suppression of feedback between secondary and primary visual cortex was suggested by the reduction of directional index of transfer entropy overall (P = 0.001; 89% decrease at 8% desflurane from 0.11 ± 0.18 a.u. at baseline) and specifically, in layer five (P = 0.001; 108% decrease at 8% desflurane from 0.12 ± 0.19 a.u. at baseline). CONCLUSIONS Desflurane anesthesia reduces neuronal interactions in visual cortex with a preferential effect on feedback. The findings suggest that neuronal disconnection occurs locally, among hierarchical sensory regions, which may contribute to global functional disconnection underlying anesthetic-induced unconsciousness.
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Mashour GA, Roelfsema P, Changeux JP, Dehaene S. Conscious Processing and the Global Neuronal Workspace Hypothesis. Neuron 2020; 105:776-798. [PMID: 32135090 PMCID: PMC8770991 DOI: 10.1016/j.neuron.2020.01.026] [Citation(s) in RCA: 378] [Impact Index Per Article: 94.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 10/31/2019] [Accepted: 01/22/2020] [Indexed: 10/24/2022]
Abstract
We review the central tenets and neuroanatomical basis of the global neuronal workspace (GNW) hypothesis, which attempts to account for the main scientific observations regarding the elementary mechanisms of conscious processing in the human brain. The GNW hypothesis proposes that, in the conscious state, a non-linear network ignition associated with recurrent processing amplifies and sustains a neural representation, allowing the corresponding information to be globally accessed by local processors. We examine this hypothesis in light of recent data that contrast brain activity evoked by either conscious or non-conscious contents, as well as during conscious or non-conscious states, particularly general anesthesia. We also discuss the relationship between the intertwined concepts of conscious processing, attention, and working memory.
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Affiliation(s)
- George A Mashour
- Center for Consciousness Science, Neuroscience Graduate Program, and Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA
| | - Pieter Roelfsema
- Department of Vision & Cognition, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA, Amsterdam, the Netherlands; Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, the Netherlands; Department of Psychiatry, Academic Medical Center, Amsterdam, the Netherlands
| | - Jean-Pierre Changeux
- CNRS UMR 3571, Institut Pasteur, 75724 Paris, France; Collège de France, 11 Place Marcelin Berthelot, 75005 Paris, France; Kavli Institute for Brain & Mind, University of California, San Diego, La Jolla, CA, USA.
| | - Stanislas Dehaene
- Collège de France, 11 Place Marcelin Berthelot, 75005 Paris, France; Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France.
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22
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Mashour GA. Role of cortical feedback signalling in consciousness and anaesthetic-induced unconsciousness. Br J Anaesth 2019; 123:404-405. [DOI: 10.1016/j.bja.2019.07.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Accepted: 07/01/2019] [Indexed: 12/20/2022] Open
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Affiliation(s)
- George A Mashour
- From the Center for Consciousness Science, Department of Anesthesiology, University of Michigan, Ann Arbor, Michigan
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