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Altmayer V, Sangare A, Calligaris C, Puybasset L, Perlbarg V, Naccache L, Sitt JD, Rohaut B. Functional and structural brain connectivity in disorders of consciousness. Brain Struct Funct 2024:10.1007/s00429-024-02839-8. [PMID: 39052096 DOI: 10.1007/s00429-024-02839-8] [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: 11/01/2023] [Accepted: 07/12/2024] [Indexed: 07/27/2024]
Abstract
Brain connectivity, allowing information to be shared between distinct cortical areas and thus to be processed in an integrated way, has long been considered critical for consciousness. However, the relationship between functional intercortical interactions and the structural connections thought to underlie them is poorly understood. In the present work, we explore both functional (with an EEG-based metric: the median weighted symbolic mutual information in the theta band) and structural (with a brain MRI-based metric: fractional anisotropy) connectivities in a cohort of 78 patients with disorders of consciousness. Both metrics could distinguish patients in a vegetative state from patients in minimally conscious state. Crucially, we discovered a significant positive correlation between functional and structural connectivities. Furthermore, we showed that this structure-function relationship is more specifically observed when considering structural connectivity within the intra- and inter-hemispheric long-distance cortico-cortical bundles involved in the Global Neuronal Workspace (GNW) theory of consciousness, thus supporting predictions of this model. Altogether, these results support the interest of multimodal assessments of brain connectivity in refining the diagnostic evaluation of patients with disorders of consciousness.
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Affiliation(s)
- Victor Altmayer
- Sorbonne University, Paris, F-75013, France
- Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Neuro-ICU, Paris, F-75013, France
| | - Aude Sangare
- Sorbonne University, Paris, F-75013, France
- Department of Neurophysiology, AP-HP, Pitié-Salpêtrière Hospital, Paris, F-75013, France
- PICNIC-Lab, Paris Brain Institute, (ICM), INSERM, CNRS, Hôpital Pitié Salpêtrière, 47 bvd de l'hôpital, Paris, F-75013, France
| | - Charlotte Calligaris
- Sorbonne University, Paris, F-75013, France
- Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Neuro-ICU, Paris, F-75013, France
| | - Louis Puybasset
- Sorbonne University, Paris, F-75013, France
- Department of Neuro-anesthesiology and Neurocritical Care, AP-HP, Pitié-Salpêtrière Hospital, Paris, F-75013, France
| | | | - Lionel Naccache
- Sorbonne University, Paris, F-75013, France
- Department of Neurophysiology, AP-HP, Pitié-Salpêtrière Hospital, Paris, F-75013, France
- PICNIC-Lab, Paris Brain Institute, (ICM), INSERM, CNRS, Hôpital Pitié Salpêtrière, 47 bvd de l'hôpital, Paris, F-75013, France
| | - Jacobo Diego Sitt
- PICNIC-Lab, Paris Brain Institute, (ICM), INSERM, CNRS, Hôpital Pitié Salpêtrière, 47 bvd de l'hôpital, Paris, F-75013, France
| | - Benjamin Rohaut
- Sorbonne University, Paris, F-75013, France.
- Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Neuro-ICU, Paris, F-75013, France.
- PICNIC-Lab, Paris Brain Institute, (ICM), INSERM, CNRS, Hôpital Pitié Salpêtrière, 47 bvd de l'hôpital, Paris, F-75013, France.
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Boscher F, Jumel K, Dvořáková T, Gentet LJ, Urbain N. Thalamocortical Dynamics during Rapid Eye Movement Sleep in the Mouse Somatosensory Pathway. J Neurosci 2024; 44:e0158242024. [PMID: 38769008 PMCID: PMC11209666 DOI: 10.1523/jneurosci.0158-24.2024] [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: 01/23/2024] [Revised: 05/02/2024] [Accepted: 05/13/2024] [Indexed: 05/22/2024] Open
Abstract
Rapid eye movement (REM) sleep, also referred to as paradoxical sleep for the striking resemblance of its electroencephalogram (EEG) to the one observed in wakefulness, is characterized by the occurrence of transient events such as limb twitches or facial and rapid eye movements. Here, we investigated the local activity of the primary somatosensory or barrel cortex (S1) in naturally sleeping head-fixed male mice during REM. Through local field potential recordings, we uncovered local appearances of spindle waves in the barrel cortex during REM concomitant with strong delta power, challenging the view of a wakefulness-like activity in REM. We further performed extra- and intracellular recordings of thalamic cells in head-fixed mice. Our data show high-frequency thalamic bursts of spikes and subthreshold spindle oscillations in approximately half of the neurons of the ventral posterior medial nucleus which further confirmed the thalamic origin of local cortical spindles in S1 in REM. Cortical spindle oscillations were suppressed, while thalamus spike firing increased, associated with rapid mouse whisker movements and S1 cortical activity transitioned to an activated state. During REM, the sensory thalamus and barrel cortex therefore alternate between high (wake-like) and low (non-REM sleep-like) activation states, potentially providing a neuronal substrate for mnemonic processes occurring during this paradoxical sleep stage.
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Affiliation(s)
- Flore Boscher
- Physiopathology of Sleep Networks, Université Claude Bernard-Lyon 1, Lyon 69500, France
| | - Katlyn Jumel
- Physiopathology of Sleep Networks, Université Claude Bernard-Lyon 1, Lyon 69500, France
| | - Tereza Dvořáková
- Physiopathology of Sleep Networks, Université Claude Bernard-Lyon 1, Lyon 69500, France
| | - Luc J Gentet
- Forgetting Processes and Cortical Dynamics, Lyon Neuroscience Research Center, INSERM U1028-CNRS UMR5292, Université Claude Bernard-Lyon 1, Lyon 69500, France
| | - Nadia Urbain
- Physiopathology of Sleep Networks, Université Claude Bernard-Lyon 1, Lyon 69500, France
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Yu WY, Sun TH, Hsu KC, Wang CC, Chien SY, Tsai CH, Yang YW. Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information. Comput Biol Med 2024; 176:108621. [PMID: 38763067 DOI: 10.1016/j.compbiomed.2024.108621] [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: 01/29/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/21/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.
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Affiliation(s)
- Wei-Yang Yu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Ting-Hsuan Sun
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Chia-Chun Wang
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shang-Yu Chien
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Chon-Haw Tsai
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan; Neuroscience Laboratory, Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Neuroscience and Brain Disease Center, College of Medicine, China Medical University, 40402, Taichung, Taiwan
| | - Yu-Wan Yang
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan.
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Cai L, Wei X, Qing Y, Lu M, Yi G, Wang J, Dong Y. Assessment of impaired consciousness using EEG-based connectivity features and convolutional neural networks. Cogn Neurodyn 2024; 18:919-930. [PMID: 38826674 PMCID: PMC11143130 DOI: 10.1007/s11571-023-09944-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 12/18/2022] [Accepted: 02/10/2023] [Indexed: 03/05/2023] Open
Abstract
Growing electroencephalogram (EEG) studies have linked the abnormities of functional brain networks with disorders of consciousness (DOC). However, due to network data's high-dimensional and non-Euclidean properties, it is difficult to exploit the brain connectivity information that can effectively detect the consciousness levels of DOC patients via deep learning. To take maximum advantage of network information in assessing impaired consciousness, we utilized the functional connectivity with convolutional neural network (CNN) and employed three rearrangement schemes to improve the evaluation performance of brain networks. In addition, the gradient-weighted class activation mapping (Grad-CAM) was adopted to visualize the classification contributions of connections among different areas. We demonstrated that the classification performance was significantly enhanced by applying network rearrangement techniques compared to those obtained by the original connectivity matrix (with an accuracy of 75.0%). The highest classification accuracy (87.2%) was achieved by rearranging the alpha network based on the anatomical regions. The inter-region connections (i.e., frontal-parietal and frontal-occipital connectivity) played dominant roles in the classification of patients with different consciousness states. The effectiveness of functional connectivity in revealing individual differences in brain activity was further validated by the correlation between behavioral performance and connections among specific regions. These findings suggest that our proposed assessment model could detect the residual consciousness of patients.
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Affiliation(s)
- Lihui Cai
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Xile Wei
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Yang Qing
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Meili Lu
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China
| | - Guosheng Yi
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Yueqing Dong
- Xincheng Hospital of Tianjin University, Tianjin, China
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Becske M, Marosi C, Molnár H, Fodor Z, Farkas K, Rácz FS, Baradits M, Csukly G. Minimum spanning tree analysis of EEG resting-state functional networks in schizophrenia. Sci Rep 2024; 14:10495. [PMID: 38714807 PMCID: PMC11076461 DOI: 10.1038/s41598-024-61316-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 05/03/2024] [Indexed: 05/10/2024] Open
Abstract
Schizophrenia is a serious and complex mental disease, known to be associated with various subtle structural and functional deviations in the brain. Recently, increased attention is given to the analysis of brain-wide, global mechanisms, strongly altering the communication of long-distance brain areas in schizophrenia. Data of 32 patients with schizophrenia and 28 matched healthy control subjects were analyzed. Two minutes long 64-channel EEG recordings were registered during resting, eyes closed condition. Average connectivity strength was estimated with Weighted Phase Lag Index (wPLI) in lower frequencies: delta and theta, and Amplitude Envelope Correlation with leakage correction (AEC-c) in higher frequencies: alpha, beta, lower gamma and higher gamma. To analyze functional network topology Minimum Spanning Tree (MST) algorithms were applied. Results show that patients have weaker functional connectivity in delta and alpha frequency bands. Concerning network differences, the result of lower diameter, higher leaf number, and also higher maximum degree and maximum betweenness centrality in patients suggest a star-like, and more random network topology in patients with schizophrenia. Our findings are in accordance with some previous findings based on resting-state EEG (and fMRI) data, suggesting that MST network structure in schizophrenia is biased towards a less optimal, more centralized organization.
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Affiliation(s)
- Melinda Becske
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Csilla Marosi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Hajnalka Molnár
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Zsuzsanna Fodor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Kinga Farkas
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | | | - Máté Baradits
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary.
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Liang Z, Lan Z, Wang Y, Bai Y, He J, Wang J, Li X. The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia. J Neural Eng 2023; 20:066030. [PMID: 38055962 DOI: 10.1088/1741-2552/ad12dc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 12/06/2023] [Indexed: 12/08/2023]
Abstract
Objective.General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed.Approach.We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel-Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces.Main results.Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p< 0.001), PFC increased (p< 0.001) and PCMI decreased (p< 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p< 0.001) and nodal efficiency (NE) (p< 0.001) decreased in these two spaces. Global network indicators normalized average path length (Lave/Lr) (p< 0.01) and modularity (Q) (p< 0.05) only decreased in sensor space, while the normalized average clustering coefficient (Cave/Cr) and small-world index (σ) did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicatorsLave/LrandQwere not significantly different from the preoperative resting state in sensor space.Significance.GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.
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Affiliation(s)
- Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, People's Republic of China
| | - Zhilei Lan
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, People's Republic of China
| | - Yong Wang
- Zhuhai UM Science & Technology Research Institute, Zhuhai 519031, People's Republic of China
| | - Yang Bai
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, People's Republic of China
- Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang 330006, Jiangxi, People's Republic of China
| | - Jianghong He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Juan Wang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, People's Republic of China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, People's Republic of China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, People's Republic of China
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Türker B, Musat EM, Chabani E, Fonteix-Galet A, Maranci JB, Wattiez N, Pouget P, Sitt J, Naccache L, Arnulf I, Oudiette D. Behavioral and brain responses to verbal stimuli reveal transient periods of cognitive integration of the external world during sleep. Nat Neurosci 2023; 26:1981-1993. [PMID: 37828228 PMCID: PMC10620087 DOI: 10.1038/s41593-023-01449-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 09/05/2023] [Indexed: 10/14/2023]
Abstract
Sleep has long been considered as a state of behavioral disconnection from the environment, without reactivity to external stimuli. Here we questioned this 'sleep disconnection' dogma by directly investigating behavioral responsiveness in 49 napping participants (27 with narcolepsy and 22 healthy volunteers) engaged in a lexical decision task. Participants were instructed to frown or smile depending on the stimulus type. We found accurate behavioral responses, visible via contractions of the corrugator or zygomatic muscles, in most sleep stages in both groups (except slow-wave sleep in healthy volunteers). Across sleep stages, responses occurred more frequently when stimuli were presented during high cognitive states than during low cognitive states, as indexed by prestimulus electroencephalography. Our findings suggest that transient windows of reactivity to external stimuli exist during bona fide sleep, even in healthy individuals. Such windows of reactivity could pave the way for real-time communication with sleepers to probe sleep-related mental and cognitive processes.
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Affiliation(s)
- Başak Türker
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Paris, France
| | - Esteban Munoz Musat
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Paris, France
| | - Emma Chabani
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Paris, France
| | | | - Jean-Baptiste Maranci
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Service des Pathologies du Sommeil, National Reference Centre for Narcolepsy, Paris, France
| | - Nicolas Wattiez
- Sorbonne Université, INSERM, Neurophysiologie Respiratoire Expérimentale et Clinique, Paris, France
| | - Pierre Pouget
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Paris, France
| | - Jacobo Sitt
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Paris, France
| | - Lionel Naccache
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Service de Neurophysiologie Clinique, Paris, France
| | - Isabelle Arnulf
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Service des Pathologies du Sommeil, National Reference Centre for Narcolepsy, Paris, France
| | - Delphine Oudiette
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, INSERM, CNRS, Paris, France.
- AP-HP, Hôpital Pitié-Salpêtrière, Service des Pathologies du Sommeil, National Reference Centre for Narcolepsy, Paris, France.
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Zelmann R, Paulk AC, Tian F, Balanza Villegas GA, Dezha Peralta J, Crocker B, Cosgrove GR, Richardson RM, Williams ZM, Dougherty DD, Purdon PL, Cash SS. Differential cortical network engagement during states of un/consciousness in humans. Neuron 2023; 111:3479-3495.e6. [PMID: 37659409 PMCID: PMC10843836 DOI: 10.1016/j.neuron.2023.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 06/13/2023] [Accepted: 08/08/2023] [Indexed: 09/04/2023]
Abstract
What happens in the human brain when we are unconscious? Despite substantial work, we are still unsure which brain regions are involved and how they are impacted when consciousness is disrupted. Using intracranial recordings and direct electrical stimulation, we mapped global, network, and regional involvement during wake vs. arousable unconsciousness (sleep) vs. non-arousable unconsciousness (propofol-induced general anesthesia). Information integration and complex processing we`re reduced, while variability increased in any type of unconscious state. These changes were more pronounced during anesthesia than sleep and involved different cortical engagement. During sleep, changes were mostly uniformly distributed across the brain, whereas during anesthesia, the prefrontal cortex was the most disrupted, suggesting that the lack of arousability during anesthesia results not from just altered overall physiology but from a disconnection between the prefrontal and other brain areas. These findings provide direct evidence for different neural dynamics during loss of consciousness compared with loss of arousability.
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Affiliation(s)
- Rina Zelmann
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA.
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
| | - Fangyun Tian
- Department of Anesthesia, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick L Purdon
- Department of Anesthesia, Massachusetts General Hospital, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
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Plosnić G, Raguž M, Deletis V, Chudy D. Dysfunctional connectivity as a neurophysiologic mechanism of disorders of consciousness: a systematic review. Front Neurosci 2023; 17:1166187. [PMID: 37539385 PMCID: PMC10394244 DOI: 10.3389/fnins.2023.1166187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023] Open
Abstract
Introduction Disorders of consciousness (DOC) has been an object of numbers of research regarding the diagnosis, treatment and prognosis in last few decades. We believe that the DOC could be considered as a disconnection syndrome, although the exact mechanisms are not entirely understood. Moreover, different conceptual frameworks highly influence results interpretation. The aim of this systematic review is to assess the current knowledge regarding neurophysiological mechanisms of DOC and to establish possible influence on future clinical implications and usage. Methods We have conducted a systematic review according to PRISMA guidelines through PubMed and Cochrane databases, with studies being selected for inclusion via a set inclusion and exclusion criteria. Results Eighty-nine studies were included in this systematic review according to the selected criteria. This includes case studies, randomized controlled trials, controlled clinical trials, and observational studies with no control arms. The total number of DOC patients encompassed in the studies cited in this review is 1,533. Conclusion Connectomics and network neuroscience offer quantitative frameworks for analysing dynamic brain connectivity. Functional MRI studies show evidence of abnormal connectivity patterns and whole-brain topological reorganization, primarily affecting sensory-related resting state networks (RSNs), confirmed by EEG studies. As previously described, DOC patients are identified by diminished global information processing, i.e., network integration and increased local information processing, i.e., network segregation. Further studies using effective connectivity measurement tools instead of functional connectivity as well as the standardization of the study process are needed.
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Affiliation(s)
- Gabriela Plosnić
- Department of Pediatrics, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Marina Raguž
- Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia
- School of Medicine, Catholic University of Croatia, Zagreb, Croatia
| | - Vedran Deletis
- Albert Einstein College of Medicine, New York, NY, United States
| | - Darko Chudy
- Department of Neurosurgery, Dubrava University Hospital, Zagreb, Croatia
- School of Medicine, University of Zagreb, Zagreb, Croatia
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Adama S, Bogdan M. Assessing consciousness in patients with disorders of consciousness using soft-clustering. Brain Inform 2023; 10:16. [PMID: 37450213 PMCID: PMC10348975 DOI: 10.1186/s40708-023-00197-5] [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: 02/27/2023] [Accepted: 06/25/2023] [Indexed: 07/18/2023] Open
Abstract
Consciousness is something we experience in our everyday life, more especially between the time we wake up in the morning and go to sleep at night, but also during the rapid eye movement (REM) sleep stage. Disorders of consciousness (DoC) are states in which a person's consciousness is damaged, possibly after a traumatic brain injury. Completely locked-in syndrome (CLIS) patients, on the other hand, display covert states of consciousness. Although they appear unconscious, their cognitive functions are mostly intact. Only, they cannot externally display it due to their quadriplegia and inability to speak. Determining these patients' states constitutes a challenging task. The ultimate goal of the approach presented in this paper is to assess these CLIS patients consciousness states. EEG data from DoC patients are used here first, under the assumption that if the proposed approach is able to accurately assess their consciousness states, it will assuredly do so on CLIS patients too. This method combines different sets of features consisting of spectral, complexity and connectivity measures in order to increase the probability of correctly estimating their consciousness levels. The obtained results showed that the proposed approach was able to correctly estimate several DoC patients' consciousness levels. This estimation is intended as a step prior attempting to communicate with them, in order to maximise the efficiency of brain-computer interfaces (BCI)-based communication systems.
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Affiliation(s)
- Sophie Adama
- Department of Neuromorphe Information Processing, Leipzig University, Augustusplatz 10, Leipzig, 04109 Germany
| | - Martin Bogdan
- Department of Neuromorphe Information Processing, Leipzig University, Augustusplatz 10, Leipzig, 04109 Germany
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Frohlich J, Bayne T, Crone JS, DallaVecchia A, Kirkeby-Hinrup A, Mediano PA, Moser J, Talar K, Gharabaghi A, Preissl H. Not with a “zap” but with a “beep”: measuring the origins of perinatal experience. Neuroimage 2023; 273:120057. [PMID: 37001834 DOI: 10.1016/j.neuroimage.2023.120057] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
When does the mind begin? Infant psychology is mysterious in part because we cannot remember our first months of life, nor can we directly communicate with infants. Even more speculative is the possibility of mental life prior to birth. The question of when consciousness, or subjective experience, begins in human development thus remains incompletely answered, though boundaries can be set using current knowledge from developmental neurobiology and recent investigations of the perinatal brain. Here, we offer our perspective on how the development of a sensory perturbational complexity index (sPCI) based on auditory ("beep-and-zip"), visual ("flash-and-zip"), or even olfactory ("sniff-and-zip") cortical perturbations in place of electromagnetic perturbations ("zap-and-zip") might be used to address this question. First, we discuss recent studies of perinatal cognition and consciousness using techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and, in particular, magnetoencephalography (MEG). While newborn infants are the archetypal subjects for studying early human development, researchers may also benefit from fetal studies, as the womb is, in many respects, a more controlled environment than the cradle. The earliest possible timepoint when subjective experience might begin is likely the establishment of thalamocortical connectivity at 26 weeks gestation, as the thalamocortical system is necessary for consciousness according to most theoretical frameworks. To infer at what age and in which behavioral states consciousness might emerge following the initiation of thalamocortical pathways, we advocate for the development of the sPCI and similar techniques, based on EEG, MEG, and fMRI, to estimate the perinatal brain's state of consciousness.
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12
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Graph approaches for analysis of brain connectivity during dexmedetomidine sedation. Neurosci Lett 2023; 797:137060. [PMID: 36626961 DOI: 10.1016/j.neulet.2023.137060] [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: 10/17/2022] [Revised: 12/22/2022] [Accepted: 01/05/2023] [Indexed: 01/08/2023]
Abstract
Sedation is commonly used to relieve fear and anxiety during procedures. Dexmedetomidine (DEX), approved by the US Food and Drug Administration in 1999 for short-term sedation, is a selective alpha2-adrenoreceptor agonist. The use of DEX is increasing due to minimal respiratory depression and easy and quick awakening from sedation. Its sedative mechanisms are suggested to be related to changes in the interaction between brain regions. In this study, we used graph theory to investigate whether the altered network connection is associated with sedation. Electroencephalogram (EEG) recordings of 32 channels were acquired during awake and DEX-induced sedation for 20 participants. We extracted EEG epochs from the awake and the DEX sedation state. Using the graph theory, we compared the changes in the network connection parameters with the awake state. We observed that the slopes in 1/f dynamics, which indicate overall brain network characteristics, were greater during DEX-induced sedation compared to the awake state, suggesting a transition towards a random network behavior. In addition, network connections from the perspective of information processing were significantly disturbed in the alpha frequency band, unlike other frequency bands augmenting network connections. The alpha frequency band plays a prominent role in the function and interaction of cognitive activities. These results collectively indicate that changes in the brain network critical to cognition during DEX administration may also be related to the mechanism of sedation.
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13
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Bouchereau E, Marchi A, Hermann B, Pruvost-Robieux E, Guinard E, Legouy C, Schimpf C, Mazeraud A, Baron JC, Ramdani C, Gavaret M, Sharshar T, Turc G. Quantitative analysis of early-stage EEG reactivity predicts awakening and recovery of consciousness in patients with severe brain injury. Br J Anaesth 2023; 130:e225-e232. [PMID: 36243578 DOI: 10.1016/j.bja.2022.09.005] [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: 04/08/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Decisions of withdrawal of life-sustaining therapy for patients with severe brain injury are often based on prognostic evaluations such as analysis of electroencephalography (EEG) reactivity (EEG-R). However, EEG-R usually relies on visual assessment, which requires neurophysiological expertise and is prone to inter-rater variability. We hypothesised that quantitative analysis of EEG-R obtained 3 days after patient admission can identify new markers of subsequent awakening and consciousness recovery. METHODS In this prospective observational study of patients with severe brain injury requiring mechanical ventilation, quantitative EEG-R was assessed using standard 11-lead EEG with frequency-based (power spectral density) and functional connectivity-based (phase-lag index) analyses. Associations between awakening in the intensive care unit (ICU) and reactivity to auditory and nociceptive stimulations were assessed with logistic regression. Secondary outcomes included in-ICU mortality and 3-month Coma Recovery Scale-Revised (CRS-R) score. RESULTS Of 116 patients, 86 (74%) awoke in the ICU. Among quantitative EEG-R markers, variation in phase-lag index connectivity in the delta frequency band after noise stimulation was associated with awakening (adjusted odds ratio=0.89, 95% confidence interval: 0.81-0.97, P=0.02 corrected for multiple tests), independently of age, baseline severity, and sedation. This new marker was independently associated with improved 3-month CRS-R (adjusted β=-0.16, standard error 0.075, P=0.048), but not with mortality (adjusted odds ratio=1.08, 95% CI: 0.99-1.18, P=0.10). CONCLUSIONS An early-stage quantitative EEG-R marker was independently associated with awakening and 3-month level of consciousness in patients with severe brain injury. This promising marker based on functional connectivity will need external validation before potential integration into a multimodal prognostic model.
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Affiliation(s)
- Eléonore Bouchereau
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France.
| | - Angela Marchi
- Epileptology and Cerebral Rhythmology Department, APHM, Timone Hospital, Marseille, France
| | - Bertrand Hermann
- ICU Department, Hôpital Européen Georges Pompidou, Paris, France; Institut du Cerveau et de la Moelle épinière - ICM, Paris, France; Université Paris Cité, Paris, France
| | - Estelle Pruvost-Robieux
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France
| | - Eléonore Guinard
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France
| | - Camille Legouy
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France
| | - Caroline Schimpf
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France
| | - Aurélien Mazeraud
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Université Paris Cité, Paris, France
| | - Jean-Claude Baron
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurology Department, GHU Paris Psychiatry and Neurosciences, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
| | - Céline Ramdani
- Institut de Recherche Biomédicale des Armées (IRBA), Brétigny-sur-Orge, France
| | - Martine Gavaret
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
| | - Tarek Sharshar
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; FHU NeuroVasc, Paris, France
| | - Guillaume Turc
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurology Department, GHU Paris Psychiatry and Neurosciences, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
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Adama S, Bogdan M. Application of Soft-Clustering to Assess Consciousness in a CLIS Patient. Brain Sci 2022; 13:brainsci13010065. [PMID: 36672046 PMCID: PMC9856569 DOI: 10.3390/brainsci13010065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/12/2022] [Accepted: 12/21/2022] [Indexed: 01/01/2023] Open
Abstract
Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients' quality of life and prognosis. On the other hand, brain-computer interfaces (BCIs) provide a means for them to communicate using their brain signals. However, one major problem for such patients is the difficulty to determine if they are conscious or not at a specific time. This work aims to combine different sets of features consisting of spectral, complexity and connectivity measures, to increase the probability of correctly estimating CLIS patients' consciousness levels. The proposed approach was tested on data from one CLIS patient, which is particular in the sense that the experimenter was able to point out one time frame Δt during which he was undoubtedly conscious. Results showed that the method presented in this paper was able to detect increases and decreases of the patient's consciousness levels. More specifically, increases were observed during this Δt, corroborating the assertion of the experimenter reporting that the patient was definitely conscious then. Assessing the patients' consciousness is intended as a step prior attempting to communicate with them, in order to maximize the efficiency of BCI-based communication systems.
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Neural complexity is a common denominator of human consciousness across diverse regimes of cortical dynamics. Commun Biol 2022; 5:1374. [PMID: 36522453 PMCID: PMC9755290 DOI: 10.1038/s42003-022-04331-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 12/01/2022] [Indexed: 12/23/2022] Open
Abstract
What is the common denominator of consciousness across divergent regimes of cortical dynamics? Does consciousness show itself in decibels or in bits? To address these questions, we introduce a testbed for evaluating electroencephalogram (EEG) biomarkers of consciousness using dissociations between neural oscillations and consciousness caused by rare genetic disorders. Children with Angelman syndrome (AS) exhibit sleep-like neural dynamics during wakefulness. Conversely, children with duplication 15q11.2-13.1 syndrome (Dup15q) exhibit wake-like neural dynamics during non-rapid eye movement (NREM) sleep. To identify highly generalizable biomarkers of consciousness, we trained regularized logistic regression classifiers on EEG data from wakefulness and NREM sleep in children with AS using both entropy measures of neural complexity and spectral (i.e., neural oscillatory) EEG features. For each set of features, we then validated these classifiers using EEG from neurotypical (NT) children and abnormal EEGs from children with Dup15q. Our results show that the classification performance of entropy-based EEG biomarkers of conscious state is not upper-bounded by that of spectral EEG features, which are outperformed by entropy features. Entropy-based biomarkers of consciousness may thus be highly adaptable and should be investigated further in situations where spectral EEG features have shown limited success, such as detecting covert consciousness or anesthesia awareness.
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16
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Cruzat J, Perl YS, Escrichs A, Vohryzek J, Timmermann C, Roseman L, Luppi AI, Ibañez A, Nutt D, Carhart-Harris R, Tagliazucchi E, Deco G, Kringelbach ML. Effects of classic psychedelic drugs on turbulent signatures in brain dynamics. Netw Neurosci 2022; 6:1104-1124. [PMID: 38800462 PMCID: PMC11117113 DOI: 10.1162/netn_a_00250] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 04/06/2022] [Indexed: 05/29/2024] Open
Abstract
Psychedelic drugs show promise as safe and effective treatments for neuropsychiatric disorders, yet their mechanisms of action are not fully understood. A fundamental hypothesis is that psychedelics work by dose-dependently changing the functional hierarchy of brain dynamics, but it is unclear whether different psychedelics act similarly. Here, we investigated the changes in the brain's functional hierarchy associated with two different psychedelics (LSD and psilocybin). Using a novel turbulence framework, we were able to determine the vorticity, that is, the local level of synchronization, that allowed us to extend the standard global time-based measure of metastability to become a local-based measure of both space and time. This framework produced detailed signatures of turbulence-based hierarchical change for each psychedelic drug, revealing consistent and discriminate effects on a higher level network, that is, the default mode network. Overall, our findings directly support a prior hypothesis that psychedelics modulate (i.e., "compress") the functional hierarchy and provide a quantification of these changes for two different psychedelics. Implications for therapeutic applications of psychedelics are discussed.
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Affiliation(s)
- Josephine Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
| | - Yonatan Sanz Perl
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Jakub Vohryzek
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
| | - Christopher Timmermann
- Centre for Psychedelic Research, Division of Psychiatry, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Leor Roseman
- Centre for Psychedelic Research, Division of Psychiatry, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Andrea I. Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom
- The Alan Turing Institute, London, United Kingdom
| | - Agustin Ibañez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, and CONICET, Buenos Aires, Argentina
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA, and Trinity College Dublin (TCD), Dublin, Ireland
| | - David Nutt
- Centre for Psychedelic Research, Division of Psychiatry, Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Robin Carhart-Harris
- Centre for Psychedelic Research, Division of Psychiatry, Department of Brain Sciences, Imperial College London, London, United Kingdom
- Psychedelics Division–Neuroscape, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
- Physics Department, University of Buenos Aires, and Buenos Aires Physics Institute, Buenos Aires, Argentina
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institució Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Denmark
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17
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Cortico-cortical and thalamo-cortical connectivity during non-REM and REM sleep: Insights from intracranial recordings in humans. Clin Neurophysiol 2022; 143:84-94. [PMID: 36166901 DOI: 10.1016/j.clinph.2022.08.026] [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: 10/03/2021] [Revised: 08/23/2022] [Accepted: 08/31/2022] [Indexed: 11/23/2022]
Abstract
OBJECTIVE To study changes of thalamo-cortical and cortico-cortical connectivity during wakefulness, non-Rapid Eye Movement (non-REM) sleep, including N2 and N3 stages, and REM sleep, using stereoelectroencephalography (SEEG) recording in humans. METHODS We studied SEEG recordings of ten patients during wakefulness, non-REM sleep and REM sleep, in seven brain regions of interest including the thalamus. We calculated directed and undirected functional connectivity using a measure of non-linear correlation coefficient h2. RESULTS The thalamus was more connected to other brain regions during N2 stage and REM sleep than during N3 stage during which cortex was more connected than the thalamus. We found two significant directed links: the first from the prefrontal region to the lateral parietal region in the delta band during N3 sleep and the second from the thalamus to the insula during REM sleep. CONCLUSIONS These results showed that cortico-cortical connectivity is more prominent in N3 stage than in N2 and REM sleep. During REM sleep we found significant thalamo-insular connectivity, with a driving role of the thalamus. SIGNIFICANCE We found a pattern of cortical connectivity during N3 sleep concordant with antero-posterior traveling slow waves. The thalamus seemed particularly involved as a hub of connectivity during REM sleep.
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18
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Vrijdag XCE, van Waart H, Pullon RM, Sames C, Mitchell SJ, Sleigh JW. EEG functional connectivity is sensitive for nitrogen narcosis at 608 kPa. Sci Rep 2022; 12:4880. [PMID: 35318392 PMCID: PMC8940999 DOI: 10.1038/s41598-022-08869-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 03/14/2022] [Indexed: 12/21/2022] Open
Abstract
Divers commonly breathe air, containing nitrogen. Nitrogen under hyperbaric conditions is a narcotic gas. In dives beyond a notional threshold of 30 m depth (405 kPa) this can cause cognitive impairment, culminating in accidents due to poor decision making. Helium is known to have no narcotic effect. This study explored potential approaches to developing an electroencephalogram (EEG) functional connectivity metric to measure narcosis produced by nitrogen at hyperbaric pressures. Twelve human participants (five female) breathed air and heliox (in random order) at 284 and 608 kPa while recording 32-channel EEG and psychometric function. The degree of spatial functional connectivity, estimated using mutual information, was summarized with global efficiency. Air-breathing at 608 kPa (experienced as mild narcosis) caused a 35% increase in global efficiency compared to surface air-breathing (mean increase = 0.17, 95% CI [0.09–0.25], p = 0.001). Air-breathing at 284 kPa trended in a similar direction. Functional connectivity was modestly associated with psychometric impairment (mixed-effects model r2 = 0.60, receiver-operating-characteristic area, 0.67 [0.51–0.84], p = 0.02). Heliox breathing did not cause a significant change in functional connectivity. In conclusion, functional connectivity increased during hyperbaric air-breathing in a dose-dependent manner, but not while heliox-breathing. This suggests sensitivity to nitrogen narcosis specifically.
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Affiliation(s)
- Xavier C E Vrijdag
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private bag 92019, Auckland, 1142, New Zealand.
| | - Hanna van Waart
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private bag 92019, Auckland, 1142, New Zealand
| | - Rebecca M Pullon
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private bag 92019, Auckland, 1142, New Zealand.,Department of Anaesthesia, Waikato Hospital, Hamilton, 3240, New Zealand
| | - Chris Sames
- Slark Hyperbaric Unit, Waitemata District Health Board, Auckland, 0610, New Zealand
| | - Simon J Mitchell
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private bag 92019, Auckland, 1142, New Zealand.,Slark Hyperbaric Unit, Waitemata District Health Board, Auckland, 0610, New Zealand.,Department of Anaesthesia, Auckland City Hospital, Auckland, 1023, New Zealand
| | - Jamie W Sleigh
- Department of Anaesthesiology, School of Medicine, University of Auckland, Private bag 92019, Auckland, 1142, New Zealand.,Department of Anaesthesia, Waikato Hospital, Hamilton, 3240, New Zealand
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19
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Munoz Musat E, Rohaut B, Sangare A, Benhaiem JM, Naccache L. Hypnotic Induction of Deafness to Elementary Sounds: An Electroencephalography Case-Study and a Proposed Cognitive and Neural Scenario. Front Neurosci 2022; 16:756651. [PMID: 35368254 PMCID: PMC8969744 DOI: 10.3389/fnins.2022.756651] [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: 08/10/2021] [Accepted: 01/17/2022] [Indexed: 11/13/2022] Open
Abstract
Hypnosis can be conceived as a unique opportunity to explore how top-down effects can influence various conscious and non-conscious processes. In the field of perception, such modulatory effects have been described in distinct sensory modalities. In the present study we focused on the auditory channel and aimed at creating a radical deafness to elementary sounds by a specific hypnotic suggestion. We report here a single case-study in a highly suggestible healthy volunteer who reported a total hypnotically suggested deafness. We recorded high-density scalp EEG during an auditory odd-ball paradigm before and after hypnotic deafness suggestion. While both early auditory event-related potentials to sounds (P1) and mismatch negativity component were not affected by hypnotic deafness, we observed a total disappearance of the late P3 complex component when the subject reported being deaf. Moreover, a centro-mesial positivity was present exclusively during the hypnotic condition prior to the P3 complex. Interestingly, source localization suggested an anterior cingulate cortex (ACC) origin of this neural event. Multivariate decoding analyses confirmed and specified these findings. Resting state analyses confirmed a similar level of conscious state in both conditions, and suggested a functional disconnection between auditory areas and other cortical areas. Taken together these results suggest the following plausible scenario: (i) preserved early processing of auditory information unaffected by hypnotic suggestion, (ii) conscious setting of an inhibitory process (ACC) preventing conscious access to sounds, (iii) functional disconnection between the modular and unconscious representations of sounds and global neuronal workspace. This single subject study presents several limits that are discussed and remains open to alternative interpretations. This original proof-of-concept paves the way to a larger study that will test the predictions stemming from our theoretical model and from this first report.
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Affiliation(s)
- Esteban Munoz Musat
- INSERM U1127, CNRS 7225, Paris Brain Institute, Paris, France
- Sorbonne Université, Paris, France
- *Correspondence: Esteban Munoz Musat, ,
| | - Benjamin Rohaut
- INSERM U1127, CNRS 7225, Paris Brain Institute, Paris, France
- Sorbonne Université, Paris, France
- Department of Neurology, Groupe Hospitalier Pitié-Salpêtrière, Assistance Publique–Hôpitaux de Paris, Paris, France
| | - Aude Sangare
- INSERM U1127, CNRS 7225, Paris Brain Institute, Paris, France
- Sorbonne Université, Paris, France
| | | | - Lionel Naccache
- INSERM U1127, CNRS 7225, Paris Brain Institute, Paris, France
- Sorbonne Université, Paris, France
- Department of Neurophysiology, Groupe Hospitalier Pitié-Salpêtrière, Assistance Publique–Hôpitaux de Paris, Paris, France
- Lionel Naccache,
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20
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Strauss M, Sitt JD, Naccache L, Raimondo F. Predicting the loss of responsiveness when falling asleep in humans. Neuroimage 2022; 251:119003. [PMID: 35176491 DOI: 10.1016/j.neuroimage.2022.119003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 11/04/2021] [Accepted: 02/13/2022] [Indexed: 11/26/2022] Open
Abstract
Falling asleep is a dynamical process that is poorly defined. The period preceding sleep, characterized by the progressive alteration of behavioral responses to the environment, which may last several minutes, has no electrophysiological definition, and is embedded in the first stage of sleep (N1). We aimed at better characterizing this drowsiness period looking for neurophysiological predictors of responsiveness using electro and magnetoencephalography. Healthy participants were recorded when falling asleep, while they were presented with continuous auditory stimulations and asked to respond to deviant sounds. We analysed brain responses to sounds and markers of ongoing activity, such as information and connectivity measures, in relation to rapid fluctuations of brain rhythms observed at brain onset and participants' capabilities to respond. Results reveal a drowsiness period distinct from wakefulness and sleep, from alpha rhythms to the first sleep spindles, characterized by diverse and transient brain states that come on and off at the scale of a few seconds and closely reflects, mainly through neural processes in alpha and theta bands, decreasing probabilities to be responsive to external stimuli. Results also show that the global P300 was only present in responsive trials, regardless of vigilance states. A better consideration of the drowsiness period through a formalized classification and its specific brain markers such as described here should lead to significant advances in vigilance assessment in the future, in medicine and ecological environments.
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Affiliation(s)
- Mélanie Strauss
- Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, NeuroSpin Center, Université Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette, France; Neuropsychology and Functional Imaging Research Group (UR2NF), Center for Research in Cognition and Neurosciences (CRCN), Université Libre de Bruxelles, B-1050, Brussels, Belgium; Departments of neurology, psychiatry and sleep medicine, Cliniques Universitaires de Bruxelles, Hôpital Erasme, Université Libre de Bruxelles, B-1070, Brussels, Belgium.
| | - Jacobo D Sitt
- Institut du Cerveau et de la Moelle épinière, ICM, PICNIC Lab, F-75013, Paris, France; Inserm U 1127, F-75013, Paris, France
| | - Lionel Naccache
- Institut du Cerveau et de la Moelle épinière, ICM, PICNIC Lab, F-75013, Paris, France; Department of Neurophysiology, Hôpital de la Pitié-Salpêtrière, AP-HP, F-75013, Paris, France
| | - Federico Raimondo
- Institut du Cerveau et de la Moelle épinière, ICM, PICNIC Lab, F-75013, Paris, France; GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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21
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Abstract
Background: The wakeful brain can easily access and coordinate a large repertoire of different states—dynamics suggestive of “criticality.” Anesthesia causes loss of criticality at the level of electroencephalogram waveforms, but the criticality of brain network connectivity is less well studied. The authors hypothesized that propofol anesthesia is associated with abrupt and divergent changes in brain network connectivity for different frequencies and time scales—characteristic of a phase transition, a signature of loss of criticality. Methods: As part of a previously reported study, 16 volunteers were given propofol in slowly increasing brain concentrations, and their behavioral responsiveness was assessed. The network dynamics from 31-channel electroencephalogram data were calculated from 1 to 20 Hz using four phase and envelope amplitude–based functional connectivity metrics that covered a wide range of time scales from milliseconds to minutes. The authors calculated network global efficiency, clustering coefficient, and statistical complexity (using the Jensen–Shannon divergence) for each functional connectivity metric and compared their findings with those from an in silico Kuramoto network model. Results: The transition to anesthesia was associated with critical slowing and then abrupt profound decreases in global network efficiency of 2 Hz power envelope metrics (from mean ± SD of 0.64 ± 0.15 to 0.29 ± 0.28 absolute value, P < 0.001, for medium; and from 0.47 ± 0.13 to 0.24 ± 0.21, P < 0.001, for long time scales) but with an increase in global network efficiency for 10 Hz weighted phase lag index (from 0.30 ± 0.20 to 0.72 ± 0.06, P < 0.001). Network complexity decreased for both the 10 Hz hypersynchronous (0.44 ± 0.13 to 0.23 ± 0.08, P < 0.001), and the 2 Hz asynchronous (0.73 ± 0.08 to 0.40 ± 0.13, P < 0.001) network states. These patterns of network coupling were consistent with those of the Kuramoto model of an order–disorder phase transition. Conclusions: Around loss of behavioral responsiveness, a small increase in propofol concentrations caused a collapse of long time scale power envelope connectivity and an increase in 10 Hz phase-based connectivity—suggestive of a brain network phase transition. Temporospatial electroencephalographic analysis of brain network dynamics over a wide range of frequencies and time scales in 16 volunteers receiving slowly increasing concentrations of propofol revealed that transition to unresponsiveness was associated with a sudden rise in alpha frequency network phase synchrony anteriorly, but also a transient surge and then loss of network coupling over long (tens of seconds) time scales. Deep anesthesia was characterized by alpha waveform hypersynchrony and slow-wave power envelope dissynchrony across the whole cortex. These observations suggest that propofol anesthesia is associated with a constellation of changes in network connectivity across frequencies and time scales that are signatures of sharp and sudden transitions in the behavior of networks.
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22
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Rivera-Lillo G, Stamatakis EA, Bekinschtein TA, Menon DK, Chennu S. Delta band activity contributes to the identification of command following in disorder of consciousness. Sci Rep 2021; 11:16267. [PMID: 34381123 PMCID: PMC8357781 DOI: 10.1038/s41598-021-95818-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 07/30/2021] [Indexed: 11/09/2022] Open
Abstract
The overt or covert ability to follow commands in patients with disorders of consciousness is considered a sign of awareness and has recently been defined as cortically mediated behaviour. Despite its clinical relevance, the brain signatures of the perceptual processing supporting command following have been elusive. This multimodal study investigates the temporal spectral pattern of electrical brain activity to identify features that differentiated healthy controls from patients both able and unable to follow commands. We combined evidence from behavioural assessment, functional neuroimaging during mental imagery and high-density electroencephalography collected during auditory prediction, from 21 patients and 10 controls. We used a penalised regression model to identify command following using features from electroencephalography. We identified seven well-defined spatiotemporal signatures in the delta, theta and alpha bands that together contribute to identify DoC subjects with and without the ability to follow command, and further distinguished these groups of patients from controls. A fine-grained analysis of these seven signatures enabled us to determine that increased delta modulation at the frontal sensors was the main feature in command following patients. In contrast, higher frequency theta and alpha modulations differentiated controls from both groups of patients. Our findings highlight a key role of spatiotemporally specific delta modulation in supporting cortically mediated behaviour including the ability to follow command. However, patients able to follow commands nevertheless have marked differences in brain activity in comparison with healthy volunteers.
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Affiliation(s)
- Gonzalo Rivera-Lillo
- Neuroscience Department, Faculty of Medicine, Universidad de Chile, Santiago, Chile. .,Physical Therapy Department, Faculty of Medicine, Universidad de Chile, Santiago, Chile. .,Research and Develop Unit, Los Coihues Clinic, Santiago, Chile.
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Department of Clinical Neurosciences, Wolfson Brain Imaging Centre, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Tristan A Bekinschtein
- Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
| | - David K Menon
- Division of Anaesthesia, Department of Medicine, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Department of Clinical Neurosciences, Wolfson Brain Imaging Centre, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Srivas Chennu
- School of Computing, University of Kent, Medway, UK.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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23
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Baror S, He BJ. Spontaneous perception: a framework for task-free, self-paced perception. Neurosci Conscious 2021; 2021:niab016. [PMID: 34377535 PMCID: PMC8333690 DOI: 10.1093/nc/niab016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 05/13/2021] [Accepted: 06/15/2021] [Indexed: 11/20/2022] Open
Abstract
Flipping through social media feeds, viewing exhibitions in a museum, or walking through the botanical gardens, people consistently choose to engage with and disengage from visual content. Yet, in most laboratory settings, the visual stimuli, their presentation duration, and the task at hand are all controlled by the researcher. Such settings largely overlook the spontaneous nature of human visual experience, in which perception takes place independently from specific task constraints and its time course is determined by the observer as a self-governing agent. Currently, much remains unknown about how spontaneous perceptual experiences unfold in the brain. Are all perceptual categories extracted during spontaneous perception? Does spontaneous perception inherently involve volition? Is spontaneous perception segmented into discrete episodes? How do different neural networks interact over time during spontaneous perception? These questions are imperative to understand our conscious visual experience in daily life. In this article we propose a framework for spontaneous perception. We first define spontaneous perception as a task-free and self-paced experience. We propose that spontaneous perception is guided by four organizing principles that grant it temporal and spatial structures. These principles include coarse-to-fine processing, continuity and segmentation, agency and volition, and associative processing. We provide key suggestions illustrating how these principles may interact with one another in guiding the multifaceted experience of spontaneous perception. We point to testable predictions derived from this framework, including (but not limited to) the roles of the default-mode network and slow cortical potentials in underlying spontaneous perception. We conclude by suggesting several outstanding questions for future research, extending the relevance of this framework to consciousness and spontaneous brain activity. In conclusion, the spontaneous perception framework proposed herein integrates components in human perception and cognition, which have been traditionally studied in isolation, and opens the door to understand how visual perception unfolds in its most natural context.
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Affiliation(s)
- Shira Baror
- Neuroscience Institute, New York University School of Medicine, 435 E 30th Street, New York, NY 10016, USA
| | - Biyu J He
- Neuroscience Institute, New York University School of Medicine, 435 E 30th Street, New York, NY 10016, USA
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24
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Naccache L. Hard but so valuable to define hard criteria for empirical theories of consciousness. Cogn Neurosci 2020; 12:79-81. [PMID: 33196376 DOI: 10.1080/17588928.2020.1839038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
I congratulate Doerig, Schurger, and Herzog for their stimulating contribution for an empirical approach to theories of consciousness. I must also admit that the excellent way Global Neuronal Workspace Theory (GNWT) I've contributed to since 2001 passes the test they designed may contribute to my enthusiasm (see Table 1 of their article). In this Comment, I focus on two points: highlighting a potential epistemological weakness of their approach, and proposing how GNWT can solve the 'small network argument.'
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Affiliation(s)
- Lionel Naccache
- Department of Neurology and Neurophysiology, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
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