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Mashour GA. Anesthesia and the neurobiology of consciousness. Neuron 2024; 112:1553-1567. [PMID: 38579714 PMCID: PMC11098701 DOI: 10.1016/j.neuron.2024.03.002] [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: 02/02/2024] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 04/07/2024]
Abstract
In the 19th century, the discovery of general anesthesia revolutionized medical care. In the 21st century, anesthetics have become indispensable tools to study consciousness. Here, I review key aspects of the relationship between anesthesia and the neurobiology of consciousness, including interfaces of sleep and anesthetic mechanisms, anesthesia and primary sensory processing, the effects of anesthetics on large-scale functional brain networks, and mechanisms of arousal from anesthesia. I discuss the implications of the data derived from the anesthetized state for the science of consciousness and then conclude with outstanding questions, reflections, and future directions.
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Affiliation(s)
- George A Mashour
- Center for Consciousness Science, Department of Anesthesiology, Department of Pharmacology, Neuroscience Graduate Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
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2
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Koller DP, Schirner M, Ritter P. Human connectome topology directs cortical traveling waves and shapes frequency gradients. Nat Commun 2024; 15:3570. [PMID: 38670965 PMCID: PMC11053146 DOI: 10.1038/s41467-024-47860-x] [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: 04/29/2023] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Traveling waves and neural oscillation frequency gradients are pervasive in the human cortex. While the direction of traveling waves has been linked to brain function and dysfunction, the factors that determine this direction remain elusive. We hypothesized that structural connectivity instrength gradients - defined as the gradually varying sum of incoming connection strengths across the cortex - could shape both traveling wave direction and frequency gradients. We confirm the presence of instrength gradients in the human connectome across diverse cohorts and parcellations. Using a cortical network model, we demonstrate how these instrength gradients direct traveling waves and shape frequency gradients. Our model fits resting-state MEG functional connectivity best in a regime where instrength-directed traveling waves and frequency gradients emerge. We further show how structural subnetworks of the human connectome generate opposing wave directions and frequency gradients observed in the alpha and beta bands. Our findings suggest that structural connectivity instrength gradients affect both traveling wave direction and frequency gradients.
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Grants
- P.R. acknowledges funding from the following sources: Digital Europe Grant TEF-Health # 101100700, H2020 Research and Innovation Action Grant Human Brain Project SGA2 785907, H2020 Research and Innovation Action Grant Human Brain Project SGA3 945539, H2020 Research and Innovation Action Grant EOSC VirtualBrainCloud 826421, H2020 Research and Innovation Action Grant AISN 101057655, H2020 Research Infrastructures Grant EBRAINS-PREP 101079717, H2020 European Innovation Council PHRASE 101058240, H2020 Research Infrastructures Grant EBRAIN-Health 101058516, H2020 European Research Council Grant ERC BrainModes 683049, JPND ERA PerMed PatternCog 2522FSB904, Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative, German Research Foundation SFB 1436 (project ID 425899996), German Research Foundation SFB 1315 (project ID 327654276), German Research Foundation SFB 936 (project ID 178316478), German Research Foundation SFB-TRR 295 (project ID 424778381) German Research Foundation SPP Computational Connectomics RI 2073/6-1, RI 2073/10-2, RI 2073/9-1.
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Affiliation(s)
- Dominik P Koller
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
| | - Michael Schirner
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, 10115, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117, Berlin, Germany
- Einstein Center Digital Future, Wilhelmstraße 67, 10117, Berlin, Germany
| | - Petra Ritter
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Department of Neurology with Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience, 10115, Berlin, Germany.
- Einstein Center for Neuroscience Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Einstein Center Digital Future, Wilhelmstraße 67, 10117, Berlin, Germany.
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3
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Jauny G, Mijalkov M, Canal-Garcia A, Volpe G, Pereira J, Eustache F, Hinault T. Linking structural and functional changes during aging using multilayer brain network analysis. Commun Biol 2024; 7:239. [PMID: 38418523 PMCID: PMC10902297 DOI: 10.1038/s42003-024-05927-x] [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: 08/05/2023] [Accepted: 02/16/2024] [Indexed: 03/01/2024] Open
Abstract
Brain structure and function are intimately linked, however this association remains poorly understood and the complexity of this relationship has remained understudied. Healthy aging is characterised by heterogenous levels of structural integrity changes that influence functional network dynamics. Here, we use the multilayer brain network analysis on structural (diffusion weighted imaging) and functional (magnetoencephalography) data from the Cam-CAN database. We found that the level of similarity of connectivity patterns between brain structure and function in the parietal and temporal regions (alpha frequency band) is associated with cognitive performance in healthy older individuals. These results highlight the impact of structural connectivity changes on the reorganisation of functional connectivity associated with the preservation of cognitive function, and provide a mechanistic understanding of the concepts of brain maintenance and compensation with aging. Investigation of the link between structure and function could thus represent a new marker of individual variability, and of pathological changes.
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Affiliation(s)
- Gwendolyn Jauny
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Mite Mijalkov
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Anna Canal-Garcia
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, Goteborg University, Goteborg, Sweden
| | - Joana Pereira
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Francis Eustache
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Thomas Hinault
- Normandie Univ, UNICAEN, PSL Université Paris, EPHE, Inserm, U1077, CHU de Caen, Centre Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.
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4
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Zhuang Y, Ge Q, Li Q, Xu L, Geng X, Wang R, He J. Combined behavioral and EEG evidence for the 70 Hz frequency selection of short-term spinal cord stimulation in disorders of consciousness. CNS Neurosci Ther 2024; 30:e14388. [PMID: 37563991 PMCID: PMC10848050 DOI: 10.1111/cns.14388] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/06/2023] [Accepted: 07/23/2023] [Indexed: 08/12/2023] Open
Abstract
OBJECTIVES This study investigated the prognostic effect of electroencephalography (EEG) instant effects of single spinal cord stimulation (SCS) on clinical outcome in disorders of consciousness (DOC) and the time-dependent brain response during the recovery of consciousness prompted by SCS. METHODS Twenty three patients with DOC underwent short-term SCS (stSCS) implantation operation. Then, all patients received the postoperative EEG test including EEG record before (T1) and after (T2) single SCS session. Subsequently, 2 weeks stSCS treatment was performed and revised coma recovery scale (CRS-R) and EEG data were collected. Finally, they were classified into effective and ineffective groups at 3-month follow-up (T6). RESULTS The parietal-occipital (PO) connectivity and clustering coefficients (CC) in the beta band of the effective group at the 1 week after the treatment (T5) were found to be higher than preoperative assessment (T0). Correlation analysis showed that the change in beta CC at T1/T2 was correlated with the change in CRS-R at T0/T6. In addition, the change in PO connectivity and CC in the beta at T0/T5 were also correlated with the change in CRS-R at T0/T5. CONCLUSION SCS may facilitate the recovery of consciousness by enhancing local information interaction in posterior brain regions. And the recovery can be predicted by beta CC in the EEG test.
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Affiliation(s)
- Yutong Zhuang
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
- Department of NeurosurgeryThe Second Clinical College of Southern Medical UniversityGuangzhouChina
| | - Qianqian Ge
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
| | - Qinghua Li
- College of AnesthesiologyShanxi Medical UniversityTaiyuanChina
| | - Long Xu
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
| | - Xiaoli Geng
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
| | - Ruoqing Wang
- High School Affiliated to Renmin UniversityBeijingChina
| | - Jianghong He
- Department of NeurosurgeryBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
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5
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Ponce-Alvarez A, Deco G. The Hopf whole-brain model and its linear approximation. Sci Rep 2024; 14:2615. [PMID: 38297071 PMCID: PMC10831083 DOI: 10.1038/s41598-024-53105-0] [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: 09/16/2023] [Accepted: 01/27/2024] [Indexed: 02/02/2024] Open
Abstract
Whole-brain models have proven to be useful to understand the emergence of collective activity among neural populations or brain regions. These models combine connectivity matrices, or connectomes, with local node dynamics, noise, and, eventually, transmission delays. Multiple choices for the local dynamics have been proposed. Among them, nonlinear oscillators corresponding to a supercritical Hopf bifurcation have been used to link brain connectivity and collective phase and amplitude dynamics in different brain states. Here, we studied the linear fluctuations of this model to estimate its stationary statistics, i.e., the instantaneous and lagged covariances and the power spectral densities. This linear approximation-that holds in the case of heterogeneous parameters and time-delays-allows analytical estimation of the statistics and it can be used for fast parameter explorations to study changes in brain state, changes in brain activity due to alterations in structural connectivity, and modulations of parameter due to non-equilibrium dynamics.
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Affiliation(s)
- Adrián Ponce-Alvarez
- Departament de Matemàtiques, Universitat Politècnica de Catalunya, 08028, Barcelona, Spain.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), 08010, Barcelona, Spain
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6
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Goekoop R, de Kleijn R. Hierarchical network structure as the source of hierarchical dynamics (power-law frequency spectra) in living and non-living systems: How state-trait continua (body plans, personalities) emerge from first principles in biophysics. Neurosci Biobehav Rev 2023; 154:105402. [PMID: 37741517 DOI: 10.1016/j.neubiorev.2023.105402] [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: 06/22/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 09/25/2023]
Abstract
Living systems are hierarchical control systems that display a small world network structure. In such structures, many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a 'power-law' cluster size distribution (a mereology). Just like their structure, the dynamics of living systems shows fractal-like qualities: the timeseries of inner message passing and overt behavior contain high frequencies or 'states' (treble) that are nested within lower frequencies or 'traits' (bass), producing a power-law frequency spectrum that is known as a 'state-trait continuum' in the behavioral sciences. Here, we argue that the power-law dynamics of living systems results from their power-law network structure: organisms 'vertically encode' the deep spatiotemporal structure of their (anticipated) environments, to the effect that many small clusters near the base of the hierarchy produce high frequency signal changes and fewer larger clusters at its top produce ultra-low frequencies. Such ultra-low frequencies exert a tonic regulatory pressure that produces morphological as well as behavioral traits (i.e., body plans and personalities). Nested-modular structure causes higher frequencies to be embedded within lower frequencies, producing a power-law state-trait continuum. At the heart of such dynamics lies the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.q., earthquakes, stock market fluctuations). Since hierarchical structure produces hierarchical dynamics, the development and collapse of hierarchical structure (e.g., during maturation and disease) should leave specific traces in system dynamics (shifts in lower frequencies, i.e. morphological and behavioral traits) that may serve as early warning signs to system failure. The applications of this idea range from (bio)physics and phylogenesis to ontogenesis and clinical medicine.
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Affiliation(s)
- R Goekoop
- Free University Amsterdam, Department of Behavioral and Movement Sciences, Parnassia Academy, Parnassia Group, PsyQ, Department of Anxiety Disorders, Early Detection and Intervention Team (EDIT), Lijnbaan 4, 2512VA The Hague, the Netherlands.
| | - R de Kleijn
- Faculty of Social and Behavioral Sciences, Department of Cognitive Psychology, Pieter de la Courtgebouw, Postbus 9555, 2300 RB Leiden, the Netherlands
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7
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Kulik SD, Douw L, van Dellen E, Steenwijk MD, Geurts JJG, Stam CJ, Hillebrand A, Schoonheim MM, Tewarie P. Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model. Netw Neurosci 2023; 7:950-965. [PMID: 37781149 PMCID: PMC10473283 DOI: 10.1162/netn_a_00303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 12/24/2022] [Indexed: 10/03/2023] Open
Abstract
Computational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical brain connections. It remains unclear whether the commonly used group-averaged data can predict individual FC patterns. The Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC). Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using phase-based (phase lag index (PLI), phase locking value (PLV)), and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze their goodness of fit for individual predictions. Individual FC predictions were compared against group-averaged FC predictions, and we tested whether SC of a different participant could equally well predict participants' FC patterns. The AEC provided a better match between individually simulated and empirical FC than phase-based metrics. Correlations between simulated and empirical FC were higher using individual SC compared to group-averaged SC. Using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants' own SC. This work underlines the added value of FC simulations using individual instead of group-averaged SC for this particular computational model and could aid in a better understanding of mechanisms underlying individual functional network trajectories.
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Affiliation(s)
- S. D. Kulik
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Brain Tumour Center Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - L. Douw
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Brain Tumour Center Amsterdam, Amsterdam, The Netherlands
| | - E. van Dellen
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Utrecht, The Netherlands
| | - M. D. Steenwijk
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - J. J. G. Geurts
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - C. J. Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam The Netherlands
| | - A. Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam The Netherlands
| | - M. M. Schoonheim
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam, The Netherlands
| | - P. Tewarie
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam The Netherlands
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8
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Chen L, Yu C, Zhai J. How network structure affects the dynamics of a network of stochastic spiking neurons. CHAOS (WOODBURY, N.Y.) 2023; 33:093101. [PMID: 37656915 DOI: 10.1063/5.0164207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/14/2023] [Indexed: 09/03/2023]
Abstract
Up to now, it still remains an open question about the relation between the structure of brain networks and their functions. The effects of structure on the dynamics of neural networks are usually investigated via extensive numerical simulations, while analytical analysis is always very difficult and thus rare. In this work, we explored the effects of a random regular graph on the dynamics of a neural network of stochastic spiking neurons, which has a bistable region when fully connected. We showed by numerical simulations that as the number of each neuron's neighbors decreases, the bistable region shrinks and eventually seems to disappear, and a critical-like transition appears. In the meantime, we made analytical analysis that explains numerical results. We hope this would give some insights into how structure affects the dynamics of neural networks from a theoretical perspective, rather than merely by numerical simulations.
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Affiliation(s)
- Lei Chen
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chaojun Yu
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jian Zhai
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, China
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9
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van de Velden D, Stier C, Kotikalapudi R, Heide EC, Garnica-Agudelo D, Focke NK. Comparison of Resting-State EEG Network Analyses With and Without Parallel MRI in Genetic Generalized Epilepsy. Brain Topogr 2023; 36:750-765. [PMID: 37354244 PMCID: PMC10415462 DOI: 10.1007/s10548-023-00977-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 06/12/2023] [Indexed: 06/26/2023]
Abstract
Genetic generalized epilepsy (GGE) is conceptualized as a brain disorder involving distributed bilateral networks. To study these networks, simultaneous EEG-fMRI measurements can be used. However, inside-MRI EEG suffers from strong MR-related artifacts; it is not established whether EEG-based metrics in EEG-fMRI resting-state measurements are suitable for the analysis of group differences at source-level. We evaluated the impact of the inside-MR measurement condition on statistical group comparisons of EEG on source-level power and functional connectivity in patients with GGE versus healthy controls. We studied the cross-modal spatial relation of statistical group differences in seed-based FC derived from EEG and parallel fMRI. We found a significant increase in power and a frequency-specific change in functional connectivity for the inside MR-scanner compared to the outside MR-scanner condition. For power, we found reduced group difference between GGE and controls both in terms of statistical significance as well as effect size. Group differences for ImCoh remained similar both in terms of statistical significance as well as effect size. We found increased seed-based FC for GGE patients from the thalamus to the precuneus cortex region in fMRI, and in the theta band of simultaneous EEG. Our findings suggest that the analysis of EEG functional connectivity based on ImCoh is suitable for MR-EEG, and that relative group difference in a comparison of patients with GGE against controls are preserved. Spatial correspondence of seed-based FC group differences between the two modalities was found for the thalamus.
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Affiliation(s)
- Daniel van de Velden
- Clinic for Neurology, University Medical Center Göttingen, 37075, Göttingen, Germany.
| | - Christina Stier
- Clinic for Neurology, University Medical Center Göttingen, 37075, Göttingen, Germany
- Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research, University Medical Center Tübingen, University of Tübingen, 72076, Tübingen, Germany
| | - Raviteja Kotikalapudi
- Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research, University Medical Center Tübingen, University of Tübingen, 72076, Tübingen, Germany
- Clinic for Neurology, University Medical Center Essen/University Duisburg-Essen, 45147, Essen, Germany
| | - Ev-Christin Heide
- Clinic for Neurology, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - David Garnica-Agudelo
- Clinic for Neurology, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Niels K Focke
- Clinic for Neurology, University Medical Center Göttingen, 37075, Göttingen, Germany.
- Department of Neurology and Epileptology, Hertie Institute of Clinical Brain Research, University Medical Center Tübingen, University of Tübingen, 72076, Tübingen, Germany.
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10
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Kawai Y. Cross-frequency coupling between slow harmonics via the real brainstem oscillators: An in vivo animal study. PLoS One 2023; 18:e0289657. [PMID: 37549170 PMCID: PMC10406189 DOI: 10.1371/journal.pone.0289657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 07/23/2023] [Indexed: 08/09/2023] Open
Abstract
Brain waves of discrete rhythms (gamma to delta frequency ranges) are ubiquitously recorded and interpreted with respect to probable corresponding specific functions. The most challenging idea of interpreting varied frequencies of brain waves has been postulated as a communication mechanism in which different neuronal assemblies use specific ranges of frequencies cooperatively. One promising candidate is cross-frequency coupling (CFC), in which some neuronal assemblies efficiently utilize the fastest gamma range brain waves as an information carrier (phase-amplitude CFC); however, phase-phase CFC via the slowest delta and theta waves has rarely been described to date. Moreover, CFC has rarely been reported in the animal brainstem including humans, which most likely utilizes the slowest waves (delta and theta ranges). Harmonic waves are characterized by the presence of a fundamental frequency with several overtones, multiples of the fundamental frequency. Rat brainstem waves seemed to consist of slow harmonics with different frequencies that could cooperatively produce a phase-phase CFC. Harmonic rhythms of different frequency ranges can cross-couple with each other to sustain robust and resilient consonance via real oscillators, notwithstanding any perturbations.
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Affiliation(s)
- Yoshinori Kawai
- Adati Institute for Brain Study (AIBS), Kawaguchi, Saitama, Japan
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11
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Huang H, Rong B, Chen C, Wan Q, Liu Z, Zhou Y, Wang G, Wang H. Common and Distinct Functional Connectivity of the Orbitofrontal Cortex in Depression and Schizophrenia. Brain Sci 2023; 13:997. [PMID: 37508929 PMCID: PMC10377532 DOI: 10.3390/brainsci13070997] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/12/2023] [Accepted: 06/22/2023] [Indexed: 07/30/2023] Open
Abstract
Schizophrenia and depression are psychiatric disorders with overlapping clinical and biological features. This study aimed to identify common and distinct neuropathological mechanisms in schizophrenia and depression patients using resting-state functional magnetic resonance imaging (fMRI). The study included 28 patients with depression (DEP), 29 patients with schizophrenia (SCH), and 30 healthy control subjects (HC). Intrinsic connectivity contrast (ICC) was used to identify functional connectivity (FC) changes at the whole-brain level, and significant ICC differences were found in the bilateral orbitofrontal cortex (OFC) across all three groups. Further seed-based FC analysis indicated that compared to the DEP and HC groups, the FC between bilateral OFC and medial prefrontal cortex (MPFC), right anterior insula, and right middle frontal gyrus were significantly lower in the SCH group. Additionally, the FC between right OFC and left thalamus was decreased in both patient groups compared to the HC group. Correlation analysis showed that the FC between OFC and MPFC was positively correlated with cognitive function in the SCH group. These findings suggest that OFC connectivity plays a critical role in the pathophysiology of schizophrenia and depression and may provide new insights into the potential neural mechanisms underlying these two disorders.
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Affiliation(s)
- Huan Huang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Bei Rong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Cheng Chen
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Qirong Wan
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Zhongchun Liu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yuan Zhou
- Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Hubei Institute of Neurology and Psychiatry Research, Wuhan 430060, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Department of Psychiatry, Zhongxiang Hospital of Renmin Hospital of Wuhan University, Zhongxiang 431900, China
- Hubei Provincial Key Laboratory of Developmentally Originated Disease, Wuhan 430071, China
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12
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Stam CJ, van Nifterick AM, de Haan W, Gouw AA. Network Hyperexcitability in Early Alzheimer's Disease: Is Functional Connectivity a Potential Biomarker? Brain Topogr 2023:10.1007/s10548-023-00968-7. [PMID: 37173584 DOI: 10.1007/s10548-023-00968-7] [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: 10/13/2022] [Accepted: 04/26/2023] [Indexed: 05/15/2023]
Abstract
Network hyperexcitability (NH) is an important feature of the pathophysiology of Alzheimer's disease. Functional connectivity (FC) of brain networks has been proposed as a potential biomarker for NH. Here we use a whole brain computational model and resting-state MEG recordings to investigate the relation between hyperexcitability and FC. Oscillatory brain activity was simulated with a Stuart Landau model on a network of 78 interconnected brain regions. FC was quantified with amplitude envelope correlation (AEC) and phase coherence (PC). MEG was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Functional connectivity was determined with the corrected AECc and phase lag index (PLI), in the 4-8 Hz and the 8-13 Hz bands. The excitation/inhibition balance in the model had a strong effect on both AEC and PC. This effect was different for AEC and PC, and was influenced by structural coupling strength and frequency band. Empirical FC matrices of SCD and MCI showed a good correlation with model FC for AEC, but less so for PC. For AEC the fit was best in the hyperexcitable range. We conclude that FC is sensitive to changes in E/I balance. The AEC was more sensitive than the PLI, and results were better for the thetaband than the alpha band. This conclusion was supported by fitting the model to empirical data. Our study justifies the use of functional connectivity measures as surrogate markers for E/I balance.
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Affiliation(s)
- C J Stam
- Department of Neurology, Amsterdam Neuroscience, Clinical Neurophysiology and MEG Center, Vrij Universiteit Amsterdam, Amsterdam UMC, PO Box 7057, 1007 MB, Amsterdam, The Netherlands.
| | - A M van Nifterick
- Department of Neurology, Amsterdam Neuroscience, Clinical Neurophysiology and MEG Center, Vrij Universiteit Amsterdam, Amsterdam UMC, PO Box 7057, 1007 MB, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - W de Haan
- Department of Neurology, Amsterdam Neuroscience, Clinical Neurophysiology and MEG Center, Vrij Universiteit Amsterdam, Amsterdam UMC, PO Box 7057, 1007 MB, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - A A Gouw
- Department of Neurology, Amsterdam Neuroscience, Clinical Neurophysiology and MEG Center, Vrij Universiteit Amsterdam, Amsterdam UMC, PO Box 7057, 1007 MB, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
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13
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Feng P, Yang J, Wu Y, Liu Z. Alternating chimera states in complex networks with modular structures. CHAOS (WOODBURY, N.Y.) 2023; 33:033136. [PMID: 37003804 DOI: 10.1063/5.0132072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
Chimera, the coexistence state of synchronization and non-synchronization, widely exists in complex networks. It has a great potentially explanatory power for the unihemispheric sleep of birds and some mammals, in which the synchronizations of the hemispheres of the cerebral cortex are evolving alternately. In this study, a coupled nonlinear oscillator system with a topology of the modular complex network was constructed to simulate the left and right hemispheres of the brain. The results showed that a stable chimera, an alternating chimera, and a breathing chimera were produced when the coupling strength and connection probability of the left and right hemispheres were changed. Further, we studied the effect of noise on rich synchronous patterns and found that the alternating chimera was robust to Gaussian white noise when the strength was not very large. Finally, our study was extended to a complex network with three sub-networks, and an alternating chimera could exist in two or three sub-networks. Our research provides a deeper insight into the mechanism of brain function like unihemispheric sleep.
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Affiliation(s)
- Peihua Feng
- State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Jiayi Yang
- State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
| | - Zhilong Liu
- Hefei General Machinery Research Institute, State Key Laboratory of Compressor Technology, Hefei 230031, People's Republic of China
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14
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Desowska A, Berde CB, Cornelissen L. Emerging functional connectivity patterns during sevoflurane anaesthesia in the developing human brain. Br J Anaesth 2023; 130:e381-e390. [PMID: 35803755 DOI: 10.1016/j.bja.2022.05.033] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/09/2022] [Accepted: 05/13/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Spectral-based EEG is used to monitor anaesthetic state during surgical procedures in adults. Spectral EEG features that can resemble the patterns seen in adults emerge in children after the age of 10 months and cannot distinguish wakefulness and anaesthesia in the youngest children. There is a need to explore alternative EEG measures. We hypothesise that functional connectivity is one of the measures that can help distinguish between consciousness states in children. METHODS An EEG data set of children undergoing sevoflurane general anaesthesia (age 0-3 yr) was reanalysed using debiased weighted phase lag index as a measure of functional connectivity in wakefulness (n=38) and anaesthesia (n=73). Network topology measures were compared between states in 0- to 6-, 6- to 10-, and >10-month-old children. RESULTS Functional connectivity was reduced in anaesthesia vs wakefulness in delta band (n=cluster of 17 significant connections; P=0.013; 58% connections surviving thresholding in wakefulness and 49% in anaesthesia). Network density and node degree were lower in anaesthesia even in the youngest children (0.57 in wakefulness; 0.48 in anaesthesia; t [9]=3.39; P=0.029; G=0.98; confidence interval [CI] [0.25-1.77]). Modularity was higher in anaesthesia (0-6 months: 0.16 in wakefulness and 0.19 in anaesthesia, t [9]=-2.95, P=0.04, G=-0.85, CI [-1.60 to -0.16]; >10 months: 0.16 vs 0.21, t [13]=-6.45, P<0.001, G=-1.62, CI [-2.49 to -0.85]) and decreased with age (ρ [73]=-0.456; P<0.001). CONCLUSIONS Anaesthesia modulates functional connectivity. Increased segregation into a more modular structure in anaesthesia decreases with age as adult-like features develop. These findings advance our understanding of the network architecture underlying the effects of anaesthesia on the developing brain.
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Affiliation(s)
- Adela Desowska
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Charles B Berde
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura Cornelissen
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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15
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Dong K, Zhang D, Wei Q, Wang G, Chen X, Zhang L, Liu J. An integrated information theory index using multichannel EEG for evaluating various states of consciousness under anesthesia. Comput Biol Med 2023; 153:106480. [PMID: 36630828 DOI: 10.1016/j.compbiomed.2022.106480] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/06/2022] [Accepted: 12/25/2022] [Indexed: 12/29/2022]
Abstract
BACKGROUND The integrated information theory (IIT) of consciousness introduces a measure Φ to quantify consciousness in a physical system. Directly related to this, general anesthesia aims to induce reversible and safe loss of consciousness (LOC). We sought to propose an electroencephalogram (EEG)-based IIT index ΦEEG to evaluate various states of consciousness under general anesthesia. METHODS Based on the definition of mutual information, we estimated the ΦEEG by maximizing the integrated information under various time lags. We used the binning method to cut the nonGaussian EEG data for estimating mutual information. We tested two EEG databases collected from propofol- (n=20) and sevoflurane-induced (n=15) anesthesia, and especially, we compared the ΦEEG of drowsy (n=7) and responsive participants (n=13) under propofol anesthesia. We compared the effectiveness of ΦEEG with the estimated bispectral index (eBIS). RESULTS In all EEG frequency bands, we observed a negative correlation between ΦEEG and end-tidal sevoflurane concentration under sevoflurane-induced anesthesia (p<0.001,BF10>6000). Under propofol-induced anesthesia, drowsy participants in moderate sedation (6.96±0.26(mean±SD)) showed decreased alpha-band ΦEEG compared with baseline (7.40±0.53,p=0.016,BF10=3.58), no significant difference was observed for responsive participants. Oppositely, the responsive participants in moderate sedation (-5.32±0.38) showed decreased eBIS compared with baseline (-4.94±0.40,p=0.03,BF10=2.41). CONCLUSIONS These findings may enable monitors of the anesthetic state that can distinguish consciousness and unconsciousness rather than the changes of anesthetic concentrations. The alpha-band ΦEEG is promising for deriving the gold standard for depth of anesthesia monitoring.
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Affiliation(s)
- Kangli Dong
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, Zhejiang, China.
| | - Delin Zhang
- The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Qishun Wei
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Guozheng Wang
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Xing Chen
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Lu Zhang
- The Department of Rehabilitation, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310027, Zhejiang, China
| | - Jun Liu
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, Zhejiang, China; Research Institute of Zhejiang University-Taizhou, Taizhou 318012, Zhejiang, China.
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16
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Maschke C, Duclos C, Blain-Moraes S. Paradoxical markers of conscious levels: Effects of propofol on patients in disorders of consciousness. Front Hum Neurosci 2022; 16:992649. [PMID: 36277055 PMCID: PMC9584648 DOI: 10.3389/fnhum.2022.992649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Human consciousness is widely understood to be underpinned by rich and diverse functional networks, whose breakdown results in unconsciousness. Candidate neural correlates of anesthetic-induced unconsciousness include: (1) disrupted frontoparietal functional connectivity; (2) disrupted brain network hubs; and (3) reduced spatiotemporal complexity. However, emerging counterexamples have revealed that these markers may appear outside of the state they are associated with, challenging both their inclusion as markers of conscious level, and the theories of consciousness that rely on their evidence. In this study, we present a case series of three individuals in disorders of consciousness (DOC) who exhibit paradoxical brain responses to exposure to anesthesia. High-density electroencephalographic data were recorded from three patients with unresponsive wakefulness syndrome (UWS) while they underwent a protocol of propofol anesthesia with a targeted effect site concentration of 2 μg/ml. Network hubs and directionality of functional connectivity in the alpha frequency band (8–13 Hz), were estimated using the weighted phase lag index (wPLI) and directed phase lag index (dPLI). The spatiotemporal signal complexity was estimated using three types of Lempel-Ziv complexity (LZC). Our results illustrate that exposure to propofol anesthesia can paradoxically result in: (1) increased frontoparietal feedback-dominant connectivity; (2) posterior network hubs; and (3) increased spatiotemporal complexity. The case examples presented in this paper challenge the role of functional connectivity and spatiotemporal complexity in theories of consciousness and for the clinical evaluation of levels of human consciousness.
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Affiliation(s)
- Charlotte Maschke
- Montreal General Hospital, McGill University Health Centre, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Catherine Duclos
- Hôpital du Sacré-Cœur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l’île-de-Montréal, Montreal, QC, Canada
- Department of Anesthesiology and Pain Medicine, Université de Montréal, Montreal, QC, Canada
| | - Stefanie Blain-Moraes
- Montreal General Hospital, McGill University Health Centre, Montreal, QC, Canada
- School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada
- *Correspondence: Stefanie Blain-Moraes,
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17
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Madan Mohan V, Banerjee A. A perturbative approach to study information communication in brain networks. Netw Neurosci 2022; 6:1275-1295. [PMID: 38800461 PMCID: PMC11117119 DOI: 10.1162/netn_a_00260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 06/15/2022] [Indexed: 05/29/2024] Open
Abstract
How communication among neuronal ensembles shapes functional brain dynamics is a question of fundamental importance to neuroscience. Communication in the brain can be viewed as a product of the interaction of node activities with the structural network over which these activities flow. The study of these interactions is, however, restricted by the difficulties in describing the complex dynamics of the brain. There is thus a need to develop methods to study these network-dynamical interactions and how they impact information flow, without having to ascertain dynamics a priori or resort to restrictive analytical approaches. Here, we adapt a recently established network analysis method based on perturbations, it to a neuroscientific setting to study how information flow in the brain can raise from properties of underlying structure. For proof-of-concept, we apply the approach on in silico whole-brain models. We expound on the functional implications of the distributions of metrics that capture network-dynamical interactions, termed net influence and flow. We also study the network-dynamical interactions at the level of resting-state networks. An attractive feature of this method is its simplicity, which allows a direct translation to an experimental or clinical setting, such as for identifying targets for stimulation studies or therapeutic interventions.
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18
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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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19
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Mann‐Krzisnik D, Mitsis GD. Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic decomposition. Hum Brain Mapp 2022; 43:4045-4073. [PMID: 35567768 PMCID: PMC9374895 DOI: 10.1002/hbm.25902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 11/11/2022] Open
Abstract
The relation between electrophysiology and BOLD-fMRI requires further elucidation. One approach for studying this relation is to find time-frequency features from electrophysiology that explain the variance of BOLD time-series. Convolution of these features with a canonical hemodynamic response function (HRF) is often required to model neurovascular coupling mechanisms and thus account for time shifts between electrophysiological and BOLD-fMRI data. We propose a framework for extracting the spatial distribution of these time-frequency features while also estimating more flexible, region-specific HRFs. The core component of this method is the decomposition of a tensor containing impulse response functions using the Canonical Polyadic Decomposition. The outputs of this decomposition provide insight into the relation between electrophysiology and BOLD-fMRI and can be used to construct estimates of BOLD time-series. We demonstrated the performance of this method on simulated data while also examining the effects of simulated measurement noise and physiological confounds. Afterwards, we validated our method on publicly available task-based and resting-state EEG-fMRI data. We adjusted our method to accommodate the multisubject nature of these datasets, enabling the investigation of inter-subject variability with regards to EEG-to-BOLD neurovascular coupling mechanisms. We thus also demonstrate how EEG features for modelling the BOLD signal differ across subjects.
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Affiliation(s)
- Dylan Mann‐Krzisnik
- Graduate Program in Biological and Biomedical EngineeringMcGill UniversityMontréalQuebecCanada
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20
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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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21
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Petkoski S, Jirsa VK. Normalizing the brain connectome for communication through synchronization. Netw Neurosci 2022; 6:722-744. [PMID: 36607179 PMCID: PMC9810372 DOI: 10.1162/netn_a_00231] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/10/2022] [Indexed: 01/10/2023] Open
Abstract
Networks in neuroscience determine how brain function unfolds, and their perturbations lead to psychiatric disorders and brain disease. Brain networks are characterized by their connectomes, which comprise the totality of all connections, and are commonly described by graph theory. This approach is deeply rooted in a particle view of information processing, based on the quantification of informational bits such as firing rates. Oscillations and brain rhythms demand, however, a wave perspective of information processing based on synchronization. We extend traditional graph theory to a dual, particle-wave, perspective, integrate time delays due to finite transmission speeds, and derive a normalization of the connectome. When applied to the database of the Human Connectome Project, it explains the emergence of frequency-specific network cores including the visual and default mode networks. These findings are robust across human subjects (N = 100) and are a fundamental network property within the wave picture. The normalized connectome comprises the particle view in the limit of infinite transmission speeds and opens the applicability of graph theory to a wide range of novel network phenomena, including physiological and pathological brain rhythms. These two perspectives are orthogonal, but not incommensurable, when understood within the novel, here-proposed, generalized framework of structural connectivity.
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Affiliation(s)
- Spase Petkoski
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France,* Corresponding Authors: ;
| | - Viktor K. Jirsa
- Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France,* Corresponding Authors: ;
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22
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Li Y, Shi J, Aihara K. Mean-field analysis of Stuart-Landau oscillator networks with symmetric coupling and dynamical noise. CHAOS (WOODBURY, N.Y.) 2022; 32:063114. [PMID: 35778116 DOI: 10.1063/5.0081295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
This paper presents analyses of networks composed of homogeneous Stuart-Landau oscillators with symmetric linear coupling and dynamical Gaussian noise. With a simple mean-field approximation, the original system is transformed into a surrogate system that describes uncorrelated oscillation/fluctuation modes of the original system. The steady-state probability distribution for these modes is described using an exponential family, and the dynamics of the system are mainly determined by the eigenvalue spectrum of the coupling matrix and the noise level. The variances of the modes can be expressed as functions of the eigenvalues and noise level, yielding the relation between the covariance matrix and the coupling matrix of the oscillators. With decreasing noise, the leading mode changes from fluctuation to oscillation, generating apparent synchrony of the coupled oscillators, and the condition for such a transition is derived. Finally, the approximate analyses are examined via numerical simulation of the oscillator networks with weak coupling to verify the utility of the approximation in outlining the basic properties of the considered coupled oscillator networks. These results are potentially useful for the modeling and analysis of indirectly measured data of neurodynamics, e.g., via functional magnetic resonance imaging and electroencephalography, as a counterpart of the frequently used Ising model.
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Affiliation(s)
- Yang Li
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Jifan Shi
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
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23
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Xue X, Wu JJ, Huo BB, Xing XX, Ma J, Li YL, Wei D, Duan YJ, Shan CL, Zheng MX, Hua XY, Xu JG. Age-Related Changes in Topological Properties of Individual Brain Metabolic Networks in Rats. Front Aging Neurosci 2022; 14:895934. [PMID: 35645769 PMCID: PMC9136077 DOI: 10.3389/fnagi.2022.895934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Normal aging causes profound changes of structural degeneration and glucose hypometabolism in the human brain, even in the absence of disease. In recent years, with the extensive exploration of the topological characteristics of the human brain, related studies in rats have begun to investigate. However, age-related alterations of topological properties in individual brain metabolic network of rats remain unknown. In this study, a total of 48 healthy female Sprague–Dawley (SD) rats were used, including 24 young rats and 24 aged rats. We used Jensen-Shannon Divergence Similarity Estimation (JSSE) method for constructing individual metabolic networks to explore age-related topological properties and rich-club organization changes. Compared with the young rats, the aged rats showed significantly decreased clustering coefficient (Cp) and local efficiency (Eloc) across the whole-brain metabolic network. In terms of changes in local network measures, degree (D) and nodal efficiency (Enod) of left posterior dorsal hippocampus, and Enod of left olfactory tubercle were higher in the aged rats than in the young rats. About the rich-club analysis, the existence of rich-club organization in individual brain metabolic networks of rats was demonstrated. In addition, our findings further confirmed that rich-club connections were susceptible to aging. Relative to the young rats, the overall strength of rich-club connections was significantly reduced in the aged rats, while the overall strength of feeder and local connections was significantly increased. These findings demonstrated the age-related reorganization principle of the brain structure and improved our understanding of brain alternations during aging.
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Affiliation(s)
- Xin Xue
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Bei-Bei Huo
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiang-Xin Xing
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu-Lin Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dong Wei
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu-Jie Duan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Mou-Xiong Zheng,
| | - Xu-Yun Hua
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Xu-Yun Hua,
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
- *Correspondence: Jian-Guang Xu,
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24
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The brainstem connectome database. Sci Data 2022; 9:168. [PMID: 35414055 PMCID: PMC9005652 DOI: 10.1038/s41597-022-01219-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 02/25/2022] [Indexed: 11/29/2022] Open
Abstract
Connectivity data of the nervous system and subdivisions, such as the brainstem, cerebral cortex and subcortical nuclei, are necessary to understand connectional structures, predict effects of connectional disorders and simulate network dynamics. For that purpose, a database was built and analyzed which comprises all known directed and weighted connections within the rat brainstem. A longterm metastudy of original research publications describing tract tracing results form the foundation of the brainstem connectome (BC) database which can be analyzed directly in the framework neuroVIISAS. The BC database can be accessed directly by connectivity tables, a web-based tool and the framework. Analysis of global and local network properties, a motif analysis, and a community analysis of the brainstem connectome provides insight into its network organization. For example, we found that BC is a scale-free network with a small-world connectivity. The Louvain modularity and weighted stochastic block matching resulted in partially matching of functions and connectivity. BC modeling was performed to demonstrate signal propagation through the somatosensory pathway which is affected in Multiple sclerosis. Measurement(s) | brainstem | Technology Type(s) | tract tracing metastudy | Factor Type(s) | brain region | Sample Characteristic - Organism | Rattus rattus | Sample Characteristic - Environment | Experimental setup | Sample Characteristic - Location | Germany |
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Kim M, Harris RE, DaSilva AF, Lee U. Explosive Synchronization-Based Brain Modulation Reduces Hypersensitivity in the Brain Network: A Computational Model Study. Front Comput Neurosci 2022; 16:815099. [PMID: 35311218 PMCID: PMC8927545 DOI: 10.3389/fncom.2022.815099] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/18/2022] [Indexed: 11/29/2022] Open
Abstract
Fibromyalgia (FM) is a chronic pain condition that is characterized by hypersensitivity to multimodal sensory stimuli, widespread pain, and fatigue. We have previously proposed explosive synchronization (ES), a phenomenon wherein a small perturbation to a network can lead to an abrupt state transition, as a potential mechanism of the hypersensitive FM brain. Therefore, we hypothesized that converting a brain network from ES to general synchronization (GS) may reduce the hypersensitivity of FM brain. To find an effective brain network modulation to convert ES into GS, we constructed a large-scale brain network model near criticality (i.e., an optimally balanced state between order and disorders), which reflects brain dynamics in conscious wakefulness, and adjusted two parameters: local structural connectivity and signal randomness of target brain regions. The network sensitivity to global stimuli was compared between the brain networks before and after the modulation. We found that only increasing the local connectivity of hubs (nodes with intense connections) changes ES to GS, reducing the sensitivity, whereas other types of modulation such as decreasing local connectivity, increasing and decreasing signal randomness are not effective. This study would help to develop a network mechanism-based brain modulation method to reduce the hypersensitivity in FM.
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Affiliation(s)
- MinKyung Kim
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Richard E. Harris
- Chronic Pain and Fatigue Research Center, Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
| | - Alexandre F. DaSilva
- Headache & Orofacial Pain Effort Laboratory, Biologic & Materials Sciences Department, University of Michigan School of Dentistry, Ann Arbor, MI, United States
| | - UnCheol Lee
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States
- *Correspondence: UnCheol Lee,
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Iliopoulos AC, Papasotiriou I. Functional Complex Networks Based on Operational Architectonics: Application on Electroencephalography-Brain-computer Interface for Imagined Speech. Neuroscience 2021; 484:98-118. [PMID: 34871742 DOI: 10.1016/j.neuroscience.2021.11.045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 01/18/2023]
Abstract
A new method for analyzing brain complex dynamics and states is presented. This method constructs functional brain graphs and is comprised of two pylons: (a) Operational architectonics (OA) concept of brain and mind functioning. (b) Network neuroscience. In particular, the algorithm utilizes OA framework for a non-parametric segmentation of EEGs, which leads to the identification of change points, namely abrupt jumps in EEG amplitude, called Rapid Transition Processes (RTPs). Subsequently, the time coordinates of RTPs are used for the generation of undirected weighted complex networks fulfilling a scale-free topology criterion, from which various network metrics of brain connectivity are estimated. These metrics form feature vectors, which can be used in machine learning algorithms for classification and/or prediction. The method is tested in classification problems on an EEG-based BCI data set, acquired from individuals during imagery pronunciation tasks of various words/vowels. The classification results, based on a Naïve Bayes classifier, show that the overall accuracies were found to be above chance level in all tested cases. This method was also compared with other state-of-the-art computational approaches commonly used for functional network generation, exhibiting competitive performance. The method can be useful to neuroscientists wishing to enhance their repository of brain research algorithms.
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Affiliation(s)
- A C Iliopoulos
- Research Genetic Cancer Centre S.A. Industrial Area of Florina, 53100 Florina, Greece
| | - I Papasotiriou
- Research Genetic Cancer Centre International GmbH, Zug 6300, Switzerland.
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Avidan MS, Mashour GA. Repurposing Propofol as a Prognostic Probe for Return of Consciousness. Am J Respir Crit Care Med 2021; 205:140-142. [PMID: 34818124 PMCID: PMC8787254 DOI: 10.1164/rccm.202111-2504ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Michael S Avidan
- Washington University School of Medicine in Saint Louis, 12275, St Louis, Missouri, United States;
| | - George A Mashour
- University of Michigan Michigan Medicine, 21614, Anesthesiology, Ann Arbor, Michigan, United States
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Neurophysiologic Complexity in Children Increases with Developmental Age and Is Reduced by General Anesthesia. Anesthesiology 2021; 135:813-828. [PMID: 34491305 DOI: 10.1097/aln.0000000000003929] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Neurophysiologic complexity in the cortex has been shown to reflect changes in the level of consciousness in adults but remains incompletely understood in the developing brain. This study aimed to address changes in cortical complexity related to age and anesthetic state transitions. This study tested the hypotheses that cortical complexity would (1) increase with developmental age and (2) decrease during general anesthesia. METHODS This was a single-center, prospective, cross-sectional study of healthy (American Society of Anesthesiologists physical status I or II) children (n = 50) of age 8 to 16 undergoing surgery with general anesthesia at Michigan Medicine. This age range was chosen because it reflects a period of substantial brain network maturation. Whole scalp (16-channel), wireless electroencephalographic data were collected from the preoperative period through the recovery of consciousness. Cortical complexity was measured using the Lempel-Ziv algorithm and analyzed during the baseline, premedication, maintenance of general anesthesia, and clinical recovery periods. The effect of spectral power on Lempel-Ziv complexity was analyzed by comparing the original complexity value with those of surrogate time series generated through phase randomization that preserves power spectrum. RESULTS Baseline spatiotemporal Lempel-Ziv complexity increased with age (yr; slope [95% CI], 0.010 [0.004, 0.016]; P < 0.001); when normalized to account for spectral power, there was no significant age effect on cortical complexity (0.001 [-0.004, 0.005]; P = 0.737). General anesthesia was associated with a significant decrease in spatiotemporal complexity (median [25th, 75th]; baseline, 0.660 [0.620, 0.690] vs. maintenance, 0.459 [0.402, 0.527]; P < 0.001), and spatiotemporal complexity exceeded baseline levels during postoperative recovery (0.704 [0.642, 0.745]; P = 0.009). When normalized, there was a similar reduction in complexity during general anesthesia (baseline, 0.913 [0.887, 0.923] vs. maintenance 0.851 [0.823, 0.877]; P < 0.001), but complexity remained significantly reduced during recovery (0.873 [0.840, 0.902], P < 0.001). CONCLUSIONS Cortical complexity increased with developmental age and decreased during general anesthesia. This association remained significant when controlling for spectral changes during anesthetic-induced perturbations in consciousness but not with developmental age. EDITOR’S PERSPECTIVE
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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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Sarasso S, Casali AG, Casarotto S, Rosanova M, Sinigaglia C, Massimini M. Consciousness and complexity: a consilience of evidence. Neurosci Conscious 2021; 2021:niab023. [PMID: 38496724 PMCID: PMC10941977 DOI: 10.1093/nc/niab023] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 06/19/2021] [Accepted: 07/29/2021] [Indexed: 03/19/2024] Open
Abstract
Over the last years, a surge of empirical studies converged on complexity-related measures as reliable markers of consciousness across many different conditions, such as sleep, anesthesia, hallucinatory states, coma, and related disorders. Most of these measures were independently proposed by researchers endorsing disparate frameworks and employing different methods and techniques. Since this body of evidence has not been systematically reviewed and coherently organized so far, this positive trend has remained somewhat below the radar. The aim of this paper is to make this consilience of evidence in the science of consciousness explicit. We start with a systematic assessment of the growing literature on complexity-related measures and identify their common denominator, tracing it back to core theoretical principles and predictions put forward more than 20 years ago. In doing this, we highlight a consistent trajectory spanning two decades of consciousness research and provide a provisional taxonomy of the present literature. Finally, we consider all of the above as a positive ground to approach new questions and devise future experiments that may help consolidate and further develop a promising field where empirical research on consciousness appears to have, so far, naturally converged.
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Affiliation(s)
- Simone Sarasso
- Department of Biomedical and Clinical Sciences ‘L. Sacco’, University of Milan, Milan 20157, Italy
| | - Adenauer Girardi Casali
- Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, Sao Jose dos Campos, 12247-014, Brazil
| | - Silvia Casarotto
- Department of Biomedical and Clinical Sciences ‘L. Sacco’, University of Milan, Milan 20157, Italy
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan 20148, Italy
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences ‘L. Sacco’, University of Milan, Milan 20157, Italy
| | | | - Marcello Massimini
- Department of Biomedical and Clinical Sciences ‘L. Sacco’, University of Milan, Milan 20157, Italy
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan 20148, Italy
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Wei HT, Francois-Nienaber A, Deschamps T, Bellana B, Hebscher M, Sivaratnam G, Zadeh M, Meltzer JA. Sensitivity of amplitude and phase based MEG measures of interhemispheric connectivity during unilateral finger movements. Neuroimage 2021; 242:118457. [PMID: 34363959 DOI: 10.1016/j.neuroimage.2021.118457] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/03/2021] [Accepted: 08/04/2021] [Indexed: 11/16/2022] Open
Abstract
Interactions between different brain regions can be revealed by dependencies between their neuronal oscillations. We examined the sensitivity of different oscillatory connectivity measures in revealing interhemispheric interactions between primary motor cortices (M1s) during unilateral finger movements. Based on frequency, amplitude, and phase of the oscillations, a number of metrics have been developed to measure connectivity between brain regions, and each metric has its own strengths, weaknesses, and pitfalls. Taking advantage of the well-known movement-related modulations of oscillatory amplitude in M1s, this study compared and contrasted a number of leading connectivity metrics during distinct phases of oscillatory power changes. Between M1s during unilateral movements, we found that phase-based metrics were effective at revealing connectivity during the beta (15-35 Hz) rebound period linked to movement termination, but not during the early period of beta desynchronization occurring during the movement itself. Amplitude correlation metrics revealed robust connectivity during both periods. Techniques for estimating the direction of connectivity had limited success. Granger Causality was not well suited to studying these connections because it was strongly confounded by differences in signal-to-noise ratio linked to modulation of beta amplitude occurring during the task. Phase slope index was suggestive but not conclusive of a unidirectional influence between motor cortices during the beta rebound. Our findings suggest that a combination of amplitude and phase-based metrics is likely required to fully characterize connectivity during task protocols that involve modulation of oscillatory power, and that amplitude-based metrics appear to be more sensitive despite the lack of directional information.
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Affiliation(s)
- Hsi T Wei
- Department of Psychology, University of Toronto, Canada; Rotman Research Institute, Baycrest Hospital, Canada.
| | | | | | - Buddhika Bellana
- Rotman Research Institute, Baycrest Hospital, Canada; Department of Psychological and Brain Sciences, Johns Hopkins University, United States
| | - Melissa Hebscher
- Rotman Research Institute, Baycrest Hospital, Canada; Feinberg School of Medicine, Northwestern University, United States
| | | | - Maryam Zadeh
- Rotman Research Institute, Baycrest Hospital, Canada
| | - Jed A Meltzer
- Department of Psychology, University of Toronto, Canada; Rotman Research Institute, Baycrest Hospital, Canada; Department of Speech-Language Pathology, University of Toronto, Canada
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Pariz A, Fischer I, Valizadeh A, Mirasso C. Transmission delays and frequency detuning can regulate information flow between brain regions. PLoS Comput Biol 2021; 17:e1008129. [PMID: 33857135 PMCID: PMC8049288 DOI: 10.1371/journal.pcbi.1008129] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 02/16/2021] [Indexed: 12/28/2022] Open
Abstract
Brain networks exhibit very variable and dynamical functional connectivity and flexible configurations of information exchange despite their overall fixed structure. Brain oscillations are hypothesized to underlie time-dependent functional connectivity by periodically changing the excitability of neural populations. In this paper, we investigate the role of the connection delay and the detuning between the natural frequencies of neural populations in the transmission of signals. Based on numerical simulations and analytical arguments, we show that the amount of information transfer between two oscillating neural populations could be determined by their connection delay and the mismatch in their oscillation frequencies. Our results highlight the role of the collective phase response curve of the oscillating neural populations for the efficacy of signal transmission and the quality of the information transfer in brain networks.
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Affiliation(s)
- Aref Pariz
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain
| | - Ingo Fischer
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain
| | - Alireza Valizadeh
- Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
- School of biological sciences, Institute for research in fundamental sciences (IPM), Tehran, Iran
- * E-mail: (AV); (CM)
| | - Claudio Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, Palma de Mallorca, Spain
- * E-mail: (AV); (CM)
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Brain network motifs are markers of loss and recovery of consciousness. Sci Rep 2021; 11:3892. [PMID: 33594110 PMCID: PMC7887248 DOI: 10.1038/s41598-021-83482-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 02/03/2021] [Indexed: 01/12/2023] Open
Abstract
Motifs are patterns of inter-connections between nodes of a network, and have been investigated as building blocks of directed networks. This study explored the re-organization of 3-node motifs during loss and recovery of consciousness. Nine healthy subjects underwent a 3-h anesthetic protocol while 128-channel electroencephalography (EEG) was recorded. In the alpha (8-13 Hz) band, 5-min epochs of EEG were extracted for: Baseline; Induction; Unconscious; 30-, 10- and 5-min pre-recovery of responsiveness; 30- and 180-min post-recovery of responsiveness. We constructed a functional brain network using the weighted and directed phase lag index, on which we calculated the frequency and topology of 3-node motifs. Three motifs (motifs 1, 2 and 5) were significantly present across participants and epochs, when compared to random networks (p < 0.05). The topology of motifs 1 and 5 changed significantly between responsive and unresponsive epochs (p-values < 0.01; Kendall's W = 0.664 (motif 1) and 0.529 (motif 5)). Motif 1 was constituted of long-range chain-like connections, while motif 5 was constituted of short-range, loop-like connections. Our results suggest that anesthetic-induced unconsciousness is associated with a topological re-organization of network motifs. As motif topological re-organization may precede (motif 5) or accompany (motif 1) the return of responsiveness, motifs could contribute to the understanding of the neural correlates of consciousness.
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Higher-order sensorimotor circuit of the brain's global network supports human consciousness. Neuroimage 2021; 231:117850. [PMID: 33582277 DOI: 10.1016/j.neuroimage.2021.117850] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 12/29/2020] [Accepted: 02/08/2021] [Indexed: 12/17/2022] Open
Abstract
Consciousness is a mental characteristic of the human mind, whose exact neural features remain unclear. We aimed to identify the critical nodes within the brain's global functional network that support consciousness. To that end, we collected a large fMRI resting state dataset with subjects in at least one of the following three consciousness states: preserved (including the healthy awake state, and patients with a brain injury history (BI) that is fully conscious), reduced (including the N1-sleep state, and minimally conscious state), and lost (including the N3-sleep state, anesthesia, and unresponsive wakefulness state). We also included a unique dataset of subjects in rapid eye movement sleep state (REM-sleep) to test for the presence of consciousness with minimum movements and sensory input. To identify critical nodes, i.e., hubs, within the brain's global functional network, we used a graph-theoretical measure of degree centrality conjoined with ROI-based functional connectivity. Using these methods, we identified various higher-order sensory and motor regions including the supplementary motor area, bilateral supramarginal gyrus (part of inferior parietal lobule), supragenual/dorsal anterior cingulate cortex, and left middle temporal gyrus, that could be important hubs whose degree centrality was significantly reduced when consciousness was reduced or absent. Additionally, we identified a sensorimotor circuit, in which the functional connectivity among these regions was significantly correlated with levels of consciousness across the different groups, and remained present in the REM-sleep group. Taken together, we demonstrated that regions forming a higher-order sensorimotor integration circuit are involved in supporting consciousness within the brain's global functional network. That offers novel and more mechanism-guided treatment targets for disorders of consciousness.
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Zierau M, Li D, Lapointe AP, Ip KI, McKinney AM, Thompson A, Puglia MP, Vlisides PE. Cortical Oscillations and Connectivity During Postoperative Recovery. J Neurosurg Anesthesiol 2021; 33:87-91. [PMID: 31436606 PMCID: PMC7031022 DOI: 10.1097/ana.0000000000000636] [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] [Indexed: 11/26/2022]
Abstract
BACKGROUND The objective of this study was to test whether postoperative electroencephalographic (EEG) biomarkers, parietal alpha power and frontal-parietal connectivity, were associated with measures of clinical recovery in adult surgical patients. METHODS This is a secondary analysis of a prospective cohort study that analyzed intraoperative connectivity patterns in adult surgical patients (N=53). Wireless, whole-scalp EEG data were collected in the postanesthesia care unit and assessed for relevance to clinical and neurocognitive recovery. Parietal alpha power and frontal-parietal connectivity (estimated by weighted phase lag index) were tested for associations with postanesthesia care unit discharge readiness and University of Michigan Sedation Scale scores upon postoperative admission. Bivariable correlation and regression models were constructed to test for unadjusted associations, then multivariable regression models were constructed to adjust for confounding. RESULTS Postoperative EEG patterns were characterized by a predominance of alpha parietal power and frontal-parietal connectivity. Neither relative parietal alpha power (% alpha, -0.25; 95% confidence interval [CI], -1.41 to 0.90; P=0.657) nor alpha frontal-parietal connectivity (weighted phase lag index, -82; 95% CI, -237 to 73; P=0.287) were associated with time until postanesthesia discharge criteria were met. Furthermore, neither alpha power (-0.03; 95% CI, -0.07 to 0.01; P=0.206) nor alpha frontal-parietal connectivity (-4.2; 95% CI, -11 to 2.6; P=0.226) were associated with sedation scores upon initial assessment. CONCLUSIONS In a pragmatic study investigating clinically relevant endpoints of postoperative recovery, we found no correlation with surrogate measures of brain neurodynamics. These data contribute to the overall impetus of developing anesthetic-invariant and generalizable markers of brain recovery.
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Affiliation(s)
| | - Duan Li
- Department of Anesthesiology
- Center for Consciousness Science
| | - Andrew P Lapointe
- Department of Radiology, University of Calgary Cummings School of Medicine, Calgary, AB, Canada
| | - Ka I Ip
- Department of Psychology, University of Michigan, Ann Arbor, MI
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Woo JH, Honey CJ, Moon JY. Phase and amplitude dynamics of coupled oscillator systems on complex networks. CHAOS (WOODBURY, N.Y.) 2020; 30:121102. [PMID: 33380037 PMCID: PMC7714526 DOI: 10.1063/5.0031031] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 11/05/2020] [Indexed: 06/12/2023]
Abstract
We investigated locking behaviors of coupled limit-cycle oscillators with phase and amplitude dynamics. We focused on how the dynamics are affected by inhomogeneous coupling strength and by angular and radial shifts in coupling functions. We performed mean-field analyses of oscillator systems with inhomogeneous coupling strength, testing Gaussian, power-law, and brain-like degree distributions. Even for oscillators with identical intrinsic frequencies and intrinsic amplitudes, we found that the coupling strength distribution and the coupling function generated a wide repertoire of phase and amplitude dynamics. These included fully and partially locked states in which high-degree or low-degree nodes would phase-lead the network. The mean-field analytical findings were confirmed via numerical simulations. The results suggest that, in oscillator systems in which individual nodes can independently vary their amplitude over time, qualitatively different dynamics can be produced via shifts in the coupling strength distribution and the coupling form. Of particular relevance to information flows in oscillator networks, changes in the non-specific drive to individual nodes can make high-degree nodes phase-lag or phase-lead the rest of the network.
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Tewarie P, Hunt BAE, O'Neill GC, Byrne A, Aquino K, Bauer M, Mullinger KJ, Coombes S, Brookes MJ. Relationships Between Neuronal Oscillatory Amplitude and Dynamic Functional Connectivity. Cereb Cortex 2020; 29:2668-2681. [PMID: 29897408 DOI: 10.1093/cercor/bhy136] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 05/12/2018] [Accepted: 05/16/2018] [Indexed: 12/15/2022] Open
Abstract
Event-related fluctuations of neural oscillatory amplitude are reported widely in the context of cognitive processing and are typically interpreted as a marker of brain "activity". However, the precise nature of these effects remains unclear; in particular, whether such fluctuations reflect local dynamics, integration between regions, or both, is unknown. Here, using magnetoencephalography, we show that movement induced oscillatory modulation is associated with transient connectivity between sensorimotor regions. Further, in resting-state data, we demonstrate a significant association between oscillatory modulation and dynamic connectivity. A confound with such empirical measurements is that increased amplitude necessarily means increased signal-to-noise ratio (SNR): this means that the question of whether amplitude and connectivity are genuinely coupled, or whether increased connectivity is observed purely due to increased SNR is unanswered. Here, we counter this problem by analogy with computational models which show that, in the presence of global network coupling and local multistability, the link between oscillatory modulation and long-range connectivity is a natural consequence of neural networks. Our results provide evidence for the notion that connectivity is mediated by neural oscillations, and suggest that time-frequency spectrograms are not merely a description of local synchrony but also reflect fluctuations in long-range connectivity.
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Affiliation(s)
- Prejaas Tewarie
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Benjamin A E Hunt
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - George C O'Neill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Aine Byrne
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Kevin Aquino
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Markus Bauer
- School of Psychology, University of Nottingham, University Park, Nottingham, UK
| | - Karen J Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Stephen Coombes
- School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
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Decoding covert visual attention based on phase transfer entropy. Physiol Behav 2020; 222:112932. [DOI: 10.1016/j.physbeh.2020.112932] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 02/18/2020] [Accepted: 04/18/2020] [Indexed: 12/12/2022]
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39
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Do Nicotinic Receptors Modulate High-Order Cognitive Processing? Trends Neurosci 2020; 43:550-564. [DOI: 10.1016/j.tins.2020.06.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 05/19/2020] [Accepted: 06/01/2020] [Indexed: 12/19/2022]
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Murphy AC, Bertolero MA, Papadopoulos L, Lydon-Staley DM, Bassett DS. Multimodal network dynamics underpinning working memory. Nat Commun 2020; 11:3035. [PMID: 32541774 PMCID: PMC7295998 DOI: 10.1038/s41467-020-15541-0] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 03/12/2020] [Indexed: 11/24/2022] Open
Abstract
Complex human cognition arises from the integrated processing of multiple brain systems. However, little is known about how brain systems and their interactions might relate to, or perhaps even explain, human cognitive capacities. Here, we address this gap in knowledge by proposing a mechanistic framework linking frontoparietal system activity, default mode system activity, and the interactions between them, with individual differences in working memory capacity. We show that working memory performance depends on the strength of functional interactions between the frontoparietal and default mode systems. We find that this strength is modulated by the activation of two newly described brain regions, and demonstrate that the functional role of these systems is underpinned by structural white matter. Broadly, our study presents a holistic account of how regional activity, functional connections, and structural linkages together support integrative processing across brain systems in order for the brain to execute a complex cognitive process.
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Affiliation(s)
- Andrew C Murphy
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Maxwell A Bertolero
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lia Papadopoulos
- Department of Physics and Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David M Lydon-Staley
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Physics and Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Electrical & Systems Engineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Santa Fe Institute, Santa Fe, NM, 87501, USA.
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Reimann HM, Niendorf T. The (Un)Conscious Mouse as a Model for Human Brain Functions: Key Principles of Anesthesia and Their Impact on Translational Neuroimaging. Front Syst Neurosci 2020; 14:8. [PMID: 32508601 PMCID: PMC7248373 DOI: 10.3389/fnsys.2020.00008] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 01/27/2020] [Indexed: 12/11/2022] Open
Abstract
In recent years, technical and procedural advances have brought functional magnetic resonance imaging (fMRI) to the field of murine neuroscience. Due to its unique capacity to measure functional activity non-invasively, across the entire brain, fMRI allows for the direct comparison of large-scale murine and human brain functions. This opens an avenue for bidirectional translational strategies to address fundamental questions ranging from neurological disorders to the nature of consciousness. The key challenges of murine fMRI are: (1) to generate and maintain functional brain states that approximate those of calm and relaxed human volunteers, while (2) preserving neurovascular coupling and physiological baseline conditions. Low-dose anesthetic protocols are commonly applied in murine functional brain studies to prevent stress and facilitate a calm and relaxed condition among animals. Yet, current mono-anesthesia has been shown to impair neural transmission and hemodynamic integrity. By linking the current state of murine electrophysiology, Ca2+ imaging and fMRI of anesthetic effects to findings from human studies, this systematic review proposes general principles to design, apply and monitor anesthetic protocols in a more sophisticated way. The further development of balanced multimodal anesthesia, combining two or more drugs with complementary modes of action helps to shape and maintain specific brain states and relevant aspects of murine physiology. Functional connectivity and its dynamic repertoire as assessed by fMRI can be used to make inferences about cortical states and provide additional information about whole-brain functional dynamics. Based on this, a simple and comprehensive functional neurosignature pattern can be determined for use in defining brain states and anesthetic depth in rest and in response to stimuli. Such a signature can be evaluated and shared between labs to indicate the brain state of a mouse during experiments, an important step toward translating findings across species.
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Affiliation(s)
- Henning M. Reimann
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück Center for Molecular Medicine, Helmholtz Association of German Research Centers (HZ), Berlin, Germany
| | - Thoralf Niendorf
- Berlin Ultrahigh Field Facility (B.U.F.F.), Max-Delbrück Center for Molecular Medicine, Helmholtz Association of German Research Centers (HZ), Berlin, Germany
- Experimental and Clinical Research Center, A Joint Cooperation Between the Charité Medical Faculty and the Max-Delbrück Center for Molecular Medicine, Berlin, Germany
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Ponrasu K, Singh U, Sathiyadevi K, Senthilkumar DV, Chandrasekar VK. Symmetry breaking dynamics induced by mean-field density and low-pass filter. CHAOS (WOODBURY, N.Y.) 2020; 30:053120. [PMID: 32491874 DOI: 10.1063/1.5142234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 04/16/2020] [Indexed: 06/11/2023]
Abstract
The phenomenon of spontaneous symmetry breaking facilitates the onset of a plethora of nontrivial dynamical states/patterns in a wide variety of dynamical systems. Spontaneous symmetry breaking results in amplitude and phase variations in a coupled identical oscillator due to the breaking of the prevailing permutational/translational symmetry of the coupled system. Nevertheless, the role and the competing interaction of the low-pass filter and the mean-field density parameter on the symmetry breaking dynamical states are unclear and yet to be explored explicitly. The effect of low pass filtering along with the mean-field parameter is explored in conjugately coupled Stuart-Landau oscillators. The dynamical transitions are examined via bifurcation analysis. We show the emergence of a spontaneous symmetry breaking (asymmetric) oscillatory state, which coexists with a nontrivial amplitude death state. Through the basin of attraction, the multi-stable nature of the spontaneous symmetry breaking state is examined, which reveals that the asymmetric distribution of the initial state favors the spontaneous symmetry breaking dynamics, while the symmetric distribution of initial states gives rise to the nontrivial amplitude death state. In addition, the trade-off between the cut-off frequency of the low-pass filter along with the mean-field density induces and enhances the symmetry breaking dynamical states. Global dynamical transitions are discussed as a function of various system parameters. Analytical stability curves corresponding to the nontrivial amplitude death and oscillation death states are deduced.
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Affiliation(s)
- K Ponrasu
- Centre for Nonlinear Science & Engineering, School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
| | - Uday Singh
- School of Physics, Indian Institute of Science Education and Research, Thiruvananthapuram 695551, Kerala, India
| | - K Sathiyadevi
- School of Physics, Indian Institute of Science Education and Research, Thiruvananthapuram 695551, Kerala, India
| | - D V Senthilkumar
- School of Physics, Indian Institute of Science Education and Research, Thiruvananthapuram 695551, Kerala, India
| | - V K Chandrasekar
- Centre for Nonlinear Science & Engineering, School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
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Changes in Whole Brain Dynamics and Connectivity Patterns during Sevoflurane- and Propofol-induced Unconsciousness Identified by Functional Magnetic Resonance Imaging. Anesthesiology 2020; 130:898-911. [PMID: 31045899 DOI: 10.1097/aln.0000000000002704] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND A key feature of the human brain is its capability to adapt flexibly to changing external stimuli. This capability can be eliminated by general anesthesia, a state characterized by unresponsiveness, amnesia, and (most likely) unconsciousness. Previous studies demonstrated decreased connectivity within the thalamus, frontoparietal, and default mode networks during general anesthesia. We hypothesized that these alterations within specific brain networks lead to a change of communication between networks and their temporal dynamics. METHODS We conducted a pooled spatial independent component analysis of resting-state functional magnetic resonance imaging data obtained from 16 volunteers during propofol and 14 volunteers during sevoflurane general anesthesia that have been previously published. Similar to previous studies, mean z-scores of the resulting spatial maps served as a measure of the activity within a network. Additionally, correlations of associated time courses served as a measure of the connectivity between networks. To analyze the temporal dynamics of between-network connectivity, we computed the correlation matrices during sliding windows of 1 min and applied k-means clustering to the matrices during both general anesthesia and wakefulness. RESULTS Within-network activity was decreased in the default mode, attentional, and salience networks during general anesthesia (P < 0.001, range of median changes: -0.34, -0.13). Average between-network connectivity was reduced during general anesthesia (P < 0.001, median change: -0.031). Distinct between-network connectivity patterns for both wakefulness and general anesthesia were observed irrespective of the anesthetic agent (P < 0.001), and there were fewer transitions in between-network connectivity patterns during general anesthesia (P < 0.001, median number of transitions during wakefulness: 4 and during general anesthesia: 0). CONCLUSIONS These results suggest that (1) higher-order brain regions play a crucial role in the generation of specific between-network connectivity patterns and their dynamics, and (2) the capability to interact with external stimuli is represented by complex between-network connectivity patterns.
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Abstract
BACKGROUND Functional connectivity across the cortex has been posited to be important for consciousness and anesthesia, but functional connectivity patterns during the course of surgery and general anesthesia are unknown. The authors tested the hypothesis that disrupted cortical connectivity patterns would correlate with surgical anesthesia. METHODS Surgical patients (n = 53) were recruited for study participation. Whole-scalp (16-channel) wireless electroencephalographic data were prospectively collected throughout the perioperative period. Functional connectivity was assessed using weighted phase lag index. During anesthetic maintenance, the temporal dynamics of connectivity states were characterized via Markov chain analysis, and state transition probabilities were quantified. RESULTS Compared to baseline (weighted phase lag index, 0.163, ± 0.091), alpha frontal-parietal connectivity was not significantly different across the remaining anesthetic and perioperative epochs, ranging from 0.100 (± 0.041) to 0.218 (± 0.136) (P > 0.05 for all time periods). In contrast, there were significant increases in alpha prefrontal-frontal connectivity (peak = 0.201 [0.154, 0.248]; P < 0.001), theta prefrontal-frontal connectivity (peak = 0.137 [0.091, 0.182]; P < 0.001), and theta frontal-parietal connectivity (peak = 0.128 [0.084, 0.173]; P < 0.001) during anesthetic maintenance. Additionally, shifts occurred between states of high prefrontal-frontal connectivity (alpha, beta) with suppressed frontal-parietal connectivity, and high frontal-parietal connectivity (alpha, theta) with reduced prefrontal-frontal connectivity. These shifts occurred in a nonrandom manner (P < 0.05 compared to random transitions), suggesting structured transitions of connectivity during general anesthesia. CONCLUSIONS Functional connectivity patterns dynamically shift during surgery and general anesthesia but do so in a structured way. Thus, a single measure of functional connectivity will likely not be a reliable correlate of surgical anesthesia.
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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: 364] [Impact Index Per Article: 91.0] [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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Kim M, Lee U. Alpha oscillation, criticality, and responsiveness in complex brain networks. Netw Neurosci 2020; 4:155-173. [PMID: 32043048 PMCID: PMC7006877 DOI: 10.1162/netn_a_00113] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Accepted: 10/29/2019] [Indexed: 12/05/2022] Open
Abstract
Brains in unconsciousness are characterized by significantly limited responsiveness to stimuli. Even during conscious wakefulness, responsiveness is highly dependent on ongoing brain activity, specifically, of alpha oscillations (∼10 Hz). We hypothesized that the variety of brain responses to external stimuli result from the interaction between state-specific and transient alpha oscillations and stimuli. To justify this hypothesis, we simulated various alpha oscillations in the human brain network, modulating criticality (a balanced state between order and disorder), and investigated specific alpha oscillation properties (instantaneous amplitude, phase, and global synchronization) that induce a large or small response. As a result, we found that the alpha oscillations near a critical state show a more complex and long-lasting response, which is more prominent when stimuli are given to globally desynchronized and low-amplitude oscillations. We also found specific phases of alpha oscillation that barely respond to stimuli, which implies the presence of temporal windows in the alpha cycle for a large or small response. The results explain why brain responses are so variable across conscious and unconscious states and across time windows even during conscious wakefulness, and emphasize the importance of considering ongoing alpha oscillations for effective brain stimulation. Responsiveness of the brain varies depending on the brain states (wakefulness, sleep, anesthesia, and traumatic injuries) and even during wakefulness, resulting in various responses to the same stimulus. What makes those different responses across brain states and even across time windows in conscious state? What is an effective way to obtain the largest response to external stimulus? To answer those questions, we simulated various alpha oscillations (∼10 Hz) in a large-scale brain network and found state-specific alpha oscillation properties that show large or small responsiveness. Notably, the results suggest the presence of temporal windows in alpha cycle that inhibit external information integration and emphasize considering the large/small responsiveness conditions for effective brain stimulation.
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Affiliation(s)
- MinKyung Kim
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - UnCheol Lee
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
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Wang S, Li Y, Qiu S, Zhang C, Wang G, Xian J, Li T, He H. Reorganization of rich-clubs in functional brain networks during propofol-induced unconsciousness and natural sleep. NEUROIMAGE-CLINICAL 2020; 25:102188. [PMID: 32018124 PMCID: PMC6997627 DOI: 10.1016/j.nicl.2020.102188] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 12/31/2019] [Accepted: 01/18/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND General anesthesia (GA) provides an invaluable experimental tool to understand the essential neural mechanisms underlying consciousness. Previous neuroimaging studies have shown the functional integration and segregation of brain functional networks during anesthetic-induced alteration of consciousness. However, the organization pattern of hubs in functional brain networks remains unclear. Moreover, comparisons with the well-characterized physiological unconsciousness can help us understand the neural mechanisms of anesthetic-induced unconsciousness. METHODS Resting-state functional magnetic resonance imaging was performed during wakefulness, mild propofol-induced sedation (m-PIS), and deep PIS (d-PIS) with clinical unconsciousness on 8 healthy volunteers and wakefulness and natural sleep on 9 age- and sex-matched healthy volunteers. Large-scale functional brain networks of each volunteer were constructed based on 160 regions of interest. Then, rich-club organizations in brain functional networks and nodal properties (nodal strength and efficiency) were assessed and analyzed among the different states and groups. RESULTS Rich-clubs in the functional brain networks were reorganized during alteration of consciousness induced by propofol. Firstly, rich-club nodes were switched from the posterior cingulate cortex (PCC), angular gyrus, and anterior and middle insula to the inferior parietal lobule (IPL), inferior parietal sulcus (IPS), and cerebellum. When sedation was deepened to unconsciousness, the rich-club nodes were switched to the occipital and angular gyrus. These results suggest that the rich-club nodes were switched among the high-order cognitive function networks (default mode network [DMN] and fronto-parietal network [FPN]), sensory networks (occipital network [ON]), and cerebellum network (CN) from consciousness (wakefulness) to propofol-induced unconsciousness. At the same time, compared with wakefulness, local connections were switched to rich-club connections during propofol-induced unconsciousness, suggesting a strengthening of the overall information commutation of networks. Nodal efficiency of the anterior and middle insula and ventral frontal cortex was significantly decreased. Additionally, from wakefulness to natural sleep, a similar pattern of rich-club reorganization with propofol-induced unconsciousness was observed: rich-club nodes were switched from the DMN (including precuneus and PCC) to the sensorimotor network (SMN, including part of the frontal and temporal gyrus). Compared with natural sleep, nodal efficiency of the insula, frontal gyrus, PCC, and cerebellum significantly decreased during propofol-induced unconsciousness. CONCLUSIONS Our study demonstrated that the rich-club reorganization in functional brain networks is characterized by switching of rich-club nodes between the high-order cognitive and sensory and motor networks during propofol-induced alteration of consciousness and natural sleep. These findings will help understand the common neurological mechanism of pharmacological and physiological unconsciousness.
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Affiliation(s)
- Shengpei Wang
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Yun Li
- Department of Anesthesia, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Shuang Qiu
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Chuncheng Zhang
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Guyan Wang
- Department of Anesthesia, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tianzuo Li
- Department of Anesthesia, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
| | - Huiguang He
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China.
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48
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Finger H, Gast R, Gerloff C, Engel AK, König P. Probing neural networks for dynamic switches of communication pathways. PLoS Comput Biol 2019; 15:e1007551. [PMID: 31841504 PMCID: PMC6936858 DOI: 10.1371/journal.pcbi.1007551] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 12/30/2019] [Accepted: 11/18/2019] [Indexed: 12/12/2022] Open
Abstract
Dynamic communication and routing play important roles in the human brain in order to facilitate flexibility in task solving and thought processes. Here, we present a network perturbation methodology that allows investigating dynamic switching between different network pathways based on phase offsets between two external oscillatory drivers. We apply this method in a computational model of the human connectome with delay-coupled neural masses. To analyze dynamic switching of pathways, we define four new metrics that measure dynamic network response properties for pairs of stimulated nodes. Evaluating these metrics for all network pathways, we found a broad spectrum of pathways with distinct dynamic properties and switching behaviors. We show that network pathways can have characteristic timescales and thus specific preferences for the phase lag between the regions they connect. Specifically, we identified pairs of network nodes whose connecting paths can either be (1) insensitive to the phase relationship between the node pair, (2) turned on and off via changes in the phase relationship between the node pair, or (3) switched between via changes in the phase relationship between the node pair. Regarding the latter, we found that 33% of node pairs can switch their communication from one pathway to another depending on their phase offsets. This reveals a potential mechanistic role that phase offsets and coupling delays might play for the dynamic information routing via communication pathways in the brain. A big challenge in elucidating information processing in the brain is to understand the neural mechanisms that dynamically organize the communication between different brain regions in a flexible and task-dependent manner. In this theoretical study, we present an approach to investigate the routing and gating of information flow along different pathways from one region to another. We show that stimulation of the brain at two sites with different frequencies and oscillatory phases can reveal the underlying effective connectivity. This yields new insights into the underlying processes that govern dynamic switches in the communication pathways between remote sites of the brain.
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Affiliation(s)
- Holger Finger
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany
- * E-mail:
| | - Richard Gast
- Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany
- MPI for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Christian Gerloff
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andreas K. Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Peter König
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany
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Kaplan C, Minc A, Basu N, Schrepf A. Inflammation and the Central Nervous System in Inflammatory Rheumatic Disease. Curr Rheumatol Rep 2019; 21:67. [PMID: 31807958 DOI: 10.1007/s11926-019-0870-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
PURPOSE OF REVIEW To review how peripheral inflammation in rheumatic disease influences the central nervous system. We consider recent studies of rheumatic disease that employ functional and structural neuroimaging in the context of inflammation, as well as recent studies considering how immunosuppressive therapy is associated with changes in brain function and structure. RECENT FINDINGS The most compelling evidence thus far comes from studies of rheumatoid arthritis and indicates that higher levels of inflammation are associated with changes in cognitive, affective, and pain-processing brain regions, some of which may be rectified by anti-inflammatory treatment. Comorbid symptoms such as widespread pain and fatigue may also be associated with these changes. Inflammation may be associated with compensatory activation of brain regions to offset structural changes. This emerging line of evidence suggests that communication between the brain and immune system are an important and underappreciated aspect of inflammatory rheumatic disease.
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Affiliation(s)
- Chelsea Kaplan
- Chronic Pain and Fatigue Research Center, Department of Anesthesiology, University of Michigan Health System, Domino's Farms, Lobby M, 24 Frank Lloyd Wright Dr., PO Box 385, Ann Arbor, MI, 48106, USA
| | | | - Neil Basu
- Institute of Infection, Immunity & Inflammation, University of Glasgow, Glasgow, UK
| | - Andrew Schrepf
- Chronic Pain and Fatigue Research Center, Department of Anesthesiology, University of Michigan Health System, Domino's Farms, Lobby M, 24 Frank Lloyd Wright Dr., PO Box 385, Ann Arbor, MI, 48106, USA.
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50
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Luppi AI, Craig MM, Pappas I, Finoia P, Williams GB, Allanson J, Pickard JD, Owen AM, Naci L, Menon DK, Stamatakis EA. Consciousness-specific dynamic interactions of brain integration and functional diversity. Nat Commun 2019; 10:4616. [PMID: 31601811 PMCID: PMC6787094 DOI: 10.1038/s41467-019-12658-9] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 09/16/2019] [Indexed: 12/26/2022] Open
Abstract
Prominent theories of consciousness emphasise different aspects of neurobiology, such as the integration and diversity of information processing within the brain. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from awake volunteers, propofol-anaesthetised volunteers, and patients with disorders of consciousness, in order to identify consciousness-specific patterns of brain function. We demonstrate that cortical networks are especially affected by loss of consciousness during temporal states of high integration, exhibiting reduced functional diversity and compromised informational capacity, whereas thalamo-cortical functional disconnections emerge during states of higher segregation. Spatially, posterior regions of the brain’s default mode network exhibit reductions in both functional diversity and integration with the rest of the brain during unconsciousness. These results show that human consciousness relies on spatio-temporal interactions between brain integration and functional diversity, whose breakdown may represent a generalisable biomarker of loss of consciousness, with potential relevance for clinical practice. How do diversity (entropy) and integration of activity across brain regions interact to support consciousness? Here the authors show that anaesthetised individuals and patients with disorders of consciousness exhibit overlapping reductions in both diversity and integration in the brain’s default mode network.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK
| | - Ioannis Pappas
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Helen Wills Neuroscience Institute, 210 Barker Hall, University of California - Berkeley, 94720, Berkeley, CA, USA
| | - Paola Finoia
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), CB2 0QQ, Cambridge, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), CB2 0QQ, Cambridge, UK
| | - Adrian M Owen
- The Brain and Mind Institute, Western Interdisciplinary Research Building, N6A 5B7 University of Western Ontario, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Lloyd Building, Trinity College Dublin, Dublin, Dublin 2, Ireland
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.,Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), CB2 0QQ, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK. .,Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, CB2 0SP, Cambridge, UK.
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