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Liuzzi P, Cassioli T, Secci S, Hakiki B, Scarpino M, Burali R, di Palma A, Toci T, Grippo A, Cecchi F, Frosini A, Mannini A. A neurophysiological profiling of the heartbeat-evoked potential in severe acquired brain injuries: A focus on unconsciousness. Eur J Neurosci 2024. [PMID: 38797841 DOI: 10.1111/ejn.16394] [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: 09/18/2023] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 05/29/2024]
Abstract
Unconsciousness in severe acquired brain injury (sABI) patients occurs with different cognitive and neural profiles. Perturbational approaches, which enable the estimation of proxies for brain reorganization, have added a new avenue for investigating the non-behavioural diagnosis of consciousness. In this prospective observational study, we conducted a comparative analysis of the topological patterns of heartbeat-evoked potentials (HEP) between patients experiencing a prolonged disorder of consciousness (pDoC) and patients emerging from a minimally consciousness state (eMCS). A total of 219 sABI patients were enrolled, each undergoing a synchronous EEG-ECG resting-state recording, together with a standardized consciousness diagnosis. A number of graph metrics were computed before/after the HEP (Before/After) using the R-peak on the ECG signal. The peak value of the global field power of the HEP was found to be significantly higher in eMCS patients with no difference in latency. Power spectrum was not able to discriminate consciousness neither Before nor After. Node assortativity and global efficiency were found to vary with different trends at unconsciousness. Lastly, the Perturbational Complexity Index of the HEP was found to be significantly higher in eMCS patients compared with pDoC. Given that cortical elaboration of peripheral inputs may serve as a non-behavioural determinant of consciousness, we have devised a low-cost and translatable technique capable of estimating causal proxies of brain functionality with an endogenous, non-invasive stimulus. Thus, we present an effective means to enhance consciousness assessment by incorporating the interaction between the autonomic nervous system (ANS) and central nervous system (CNS) into the loop.
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Affiliation(s)
- Piergiuseppe Liuzzi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
- Istituto di BioRobotica, Scuola Superiore Sant'Anna, Pisa, Italy
| | | | - Sara Secci
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
| | - Bahia Hakiki
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
- Dipartimento di Medicina Sperimentale e Clinica, Università di Firenze, Florence, Italy
| | | | - Rachele Burali
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
| | | | - Tanita Toci
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
| | | | - Francesca Cecchi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
- Dipartimento di Medicina Sperimentale e Clinica, Università di Firenze, Florence, Italy
| | - Andrea Frosini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
- Dipartimento di Matematica Ulisse Dini, Università di Firenze, Florence, Italy
| | - Andrea Mannini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
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Secci S, Liuzzi P, Hakiki B, Burali R, Draghi F, Romoli AM, di Palma A, Scarpino M, Grippo A, Cecchi F, Frosini A, Mannini A. Low-density EEG-based Functional Connectivity Discriminates Minimally Conscious State plus from minus. Clin Neurophysiol 2024; 163:197-208. [PMID: 38761713 DOI: 10.1016/j.clinph.2024.04.021] [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: 05/04/2023] [Revised: 04/03/2024] [Accepted: 04/18/2024] [Indexed: 05/20/2024]
Abstract
OBJECTIVE Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS-). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance. METHODS In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/- patients by enrolling 57 MCS patients (MCS-: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs' metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands (α,θ,δ). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/-. RESULTS A lower level of brain activity integration was found in the MCS- group in the α band. Instead, in the δ band MCS- group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/- through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of 79% (sensitivity and specificity of 74% and 85% respectively). CONCLUSION Despite tackling the MCS+/- differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks. SIGNIFICANCE Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.
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Affiliation(s)
- Sara Secci
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, Firenze, FI, Italy
| | - Piergiuseppe Liuzzi
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, Firenze, FI, Italy; Scuola Superiore Sant'Anna, BioRobotics Institute, Viale Rinaldo Piaggio 34, Pontedera, PI, Italy
| | - Bahia Hakiki
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, Firenze, FI, Italy; Dipartimento di Medicina Sperimentale e Clinica, Largo Brambilla 3, FI, Italy.
| | - Rachele Burali
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, Firenze, FI, Italy
| | - Francesca Draghi
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, Firenze, FI, Italy
| | - Anna Maria Romoli
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, Firenze, FI, Italy
| | - Azzurra di Palma
- Dipartimento di Matematica e Informatica, Università di Firenze, Viale Morgagni 65, FI, Italy
| | - Maenia Scarpino
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, Firenze, FI, Italy
| | - Antonello Grippo
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, Firenze, FI, Italy
| | - Francesca Cecchi
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, Firenze, FI, Italy; Dipartimento di Medicina Sperimentale e Clinica, Largo Brambilla 3, FI, Italy
| | - Andrea Frosini
- Dipartimento di Matematica e Informatica, Università di Firenze, Viale Morgagni 65, FI, Italy
| | - Andrea Mannini
- IRCCS Fondazione Don Carlo Gnocchi, via di Scandicci 269, Firenze, FI, Italy
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Dagnino PC, Escrichs A, López-González A, Gosseries O, Annen J, Sanz Perl Y, Kringelbach ML, Laureys S, Deco G. Re-awakening the brain: Forcing transitions in disorders of consciousness by external in silico perturbation. PLoS Comput Biol 2024; 20:e1011350. [PMID: 38701063 PMCID: PMC11068192 DOI: 10.1371/journal.pcbi.1011350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 03/31/2024] [Indexed: 05/05/2024] Open
Abstract
A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness.
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Affiliation(s)
- Paulina Clara Dagnino
- 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
| | - Ane López-González
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau 2, University Hospital of Liège, Liège, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du Cerveau 2, University Hospital of Liège, Liège, Belgium
| | - Yonatan Sanz Perl
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
- Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, 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, Aarhus, Denmark
| | - Steven Laureys
- Joint International Research Unit on Consciousness, CERVO Brain Research Centre, University of Laval, Québec, Québec, Canada
| | - 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 Avançats (ICREA), Barcelona, Catalonia, Spain
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Gong A, Wang Q, Guo Q, Yang Y, Chen X, Hu X, Zhang Y. Variability of large timescale functional networks in patients with disorders of consciousness. Front Neurol 2024; 15:1283140. [PMID: 38434205 PMCID: PMC10905795 DOI: 10.3389/fneur.2024.1283140] [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: 08/30/2023] [Accepted: 01/24/2024] [Indexed: 03/05/2024] Open
Abstract
Objective Most brain function assessments for disorders of consciousness (DOC) utilized quantified characteristics, measured only once, ignoring the variation of patients' brain states. The study aims to investigate the brain activities of patients with DOC from a new perspective: variability of a large timescale functional network. Methods Forty-nine patients were enrolled in this study and performed a 1-week behavioral assessment. Subsequently, each patient received electroencephalography (EEG) recordings five times daily at 2-h intervals. Functional connectivity and networks were measured by weighted phase lag index and complex network parameters (characteristic path length, cluster coefficient, and betweenness centrality). The relative coefficient of variation (CV) of network parameters was measured to evaluate functional network variability. Results Functional networks of patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS) showed significantly higher segregation (characteristic path length) and lower centrality (betweenness centrality) than emerging from the minimal conscious state (EMCS) and minimal conscious state (MCS), as well as lower integration (cluster coefficient) than MCS. The functional networks of VS/UWS patients consistently presented the highest variability in segregation and integration (i.e., highest CV values of characteristic path length and cluster coefficient) on a larger time scale than MCS and EMCS. Moreover, the CV values of characteristic path length and cluster coefficient showed a significant inverse correlation with the Coma Recovery Scale-Revised scores (CRS-R). The CV values of network betweenness centrality, particularly of the cento-parietal region, showed a positive correlation with the CRS-R. Conclusion The functional networks of VS/UWS patients present the most invariant segregation and integration but divergent centrality on the large time scale networks than MCS and EMCS. Significance The variations observed within large timescale functional networks significantly correlate with the degree of consciousness impairment. This finding augments our understanding of the neurophysiological mechanisms underpinning disorders of consciousness.
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Affiliation(s)
- Anjuan Gong
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Qijun Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Qian Guo
- Hangzhou Normal University School of Nursing, Hangzhou, Zhejiang, China
| | - Ying Yang
- Hangzhou Normal University School of Nursing, Hangzhou, Zhejiang, China
| | - Xuewei Chen
- Hangzhou Normal University School of Nursing, Hangzhou, Zhejiang, China
| | - Xiaohua Hu
- Department of Rehabilitation Medicine, Armed Police Corps Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Ying Zhang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
- Hangzhou Normal University School of Nursing, Hangzhou, Zhejiang, China
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Liu G, Chi B. Technological Modalities in the Assessment and Treatment of Disorders of Consciousness. Phys Med Rehabil Clin N Am 2024; 35:109-126. [PMID: 37993182 DOI: 10.1016/j.pmr.2023.07.005] [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] [Indexed: 11/24/2023]
Abstract
Over the last 10 years, there have been rapid advances made in technologies that can be utilized in the diagnosis and treatment of patients with a disorder of consciousness (DoC). This article provides a comprehensive review of these modalities including the evidence supporting their potential use in DoC. This review specifically addresses diagnostic, non-invasive therapeutic, and invasive therapeutic technological modalities except for neuroimaging, which is discussed in another article. While technologic advances appear promising for both assessment and treatment of patients with a DoC, high-quality evidence supporting widespread clinical adoption remains limited.
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Affiliation(s)
- Gang Liu
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, No 12 Wulumuqi Middle Road, Shanghai 200040, China
| | - Bradley Chi
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, 7200 Cambridge Street, Houston, TX 77030, USA.
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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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Qin X, Chen X, Wang B, Zhao X, Tang Y, Yao L, Liang Z, He J, Li X. EEG Changes during Propofol Anesthesia Induction in Vegetative State Patients Undergoing Spinal Cord Stimulation Implantation Surgery. Brain Sci 2023; 13:1608. [PMID: 38002567 PMCID: PMC10669685 DOI: 10.3390/brainsci13111608] [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: 10/11/2023] [Revised: 11/07/2023] [Accepted: 11/17/2023] [Indexed: 11/26/2023] Open
Abstract
OBJECTIVE To compare the EEG changes in vegetative state (VS) patients and non-craniotomy, non-vegetative state (NVS) patients during general anesthesia with low-dose propofol and to find whether it affects the arousal rate of VS patients. METHODS Seven vegetative state patients (VS group: five with traumatic brain injury, two with ischemic-hypoxic VS) and five non-craniotomy, non-vegetative state patients (NVS group) treated in the Department of Neurosurgery, Peking University International Hospital from January to May 2022 were selected. All patients were induced with 0.5 mg/kg propofol, and the Bispectral Index (BIS) changes within 5 min after administration were observed. Raw EEG signals and perioperative EEG signals were collected and analyzed using EEGLAB in the MATLAB software environment, time-frequency spectrums were calculated, and EEG changes were analyzed using power spectrums. RESULTS There was no significant difference in the general data before surgery between the two groups (p > 0.05); the BIS reduction in the VS group was significantly greater than that in the NVS group at 1 min, 2 min, 3 min, 4 min, and 5 min after 0.5 mg/kg propofol induction (p < 0.05). Time-frequency spectrum analysis showed the following: prominent α band energy around 10 Hz and decreased high-frequency energy in the NVS group, decreased high-frequency energy and main energy concentrated below 10 Hz in traumatic brain injury VS patients, higher energy in the 10-20 Hz band in ischemic-hypoxic VS patients. The power spectrum showed that the brain electrical energy of the NVS group was weakened R5 min after anesthesia induction compared with 5 min before induction, mainly concentrated in the small wave peak after 10 Hz, i.e., the α band peak; the energy of traumatic brain injury VS patients was weakened after anesthesia induction, but no α band peak appeared; and in ischemic-hypoxic VS patients, there was no significant change in low-frequency energy after anesthesia induction, high-frequency energy was significantly weakened, and a clear α band peak appeared slightly after 10 Hz. Three months after the operation, follow-up visits were made to the VS group patients who had undergone SCS surgery. One patient with traumatic brain injury VS was diagnosed with MCS-, one patient with ischemic-hypoxic VS had increased their CRS-R score by 1 point, and the remaining five patients had no change in their CRS scores. CONCLUSIONS Low doses of propofol cause great differences in the EEG of different types of VS patients, which may be the unique response of damaged nerve cell residual function to propofol, and these weak responses may also be the basis of brain recovery.
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Affiliation(s)
- Xuewei Qin
- Department of Anesthesiology, Peking University International Hospital, Beijing 102206, China; (X.Q.); (X.Z.)
| | - Xuanling Chen
- Department of Anesthesiology, Peking University International Hospital, Beijing 102206, China; (X.Q.); (X.Z.)
| | - Bo Wang
- Department of Anesthesiology, Peking University International Hospital, Beijing 102206, China; (X.Q.); (X.Z.)
| | - Xin Zhao
- Department of Anesthesiology, Peking University International Hospital, Beijing 102206, China; (X.Q.); (X.Z.)
| | - Yi Tang
- Department of Anesthesiology, Peking University International Hospital, Beijing 102206, China; (X.Q.); (X.Z.)
| | - Lan Yao
- Department of Anesthesiology, Peking University International Hospital, Beijing 102206, China; (X.Q.); (X.Z.)
| | - Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
| | - Jianghong He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China;
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China;
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
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Liuzzi P, Mannini A, Hakiki B, Campagnini S, Romoli AM, Draghi F, Burali R, Scarpino M, Cecchi F, Grippo A. Brain microstate spatio-temporal dynamics as a candidate endotype of consciousness. Neuroimage Clin 2023; 41:103540. [PMID: 38101096 PMCID: PMC10727951 DOI: 10.1016/j.nicl.2023.103540] [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: 05/18/2023] [Revised: 10/02/2023] [Accepted: 11/09/2023] [Indexed: 12/17/2023]
Abstract
Consciousness can be defined as a phenomenological experience continuously evolving. Current research showed how conscious mental activity can be subdivided into a series of atomic brain states converging to a discrete spatiotemporal pattern of global neuronal firing. Using the high temporal resolution of EEG recordings in patients with a severe Acquired Brain Injury (sABI) admitted to an Intensive Rehabilitation Unit (IRU), we detected a novel endotype of consciousness from the spatiotemporal brain dynamics identified via microstate analysis. Also, we investigated whether microstate features were associated with common neurophysiological alterations. Finally, the prognostic information comprised in such descriptors was analysed in a sub-cohort of patients with prolonged Disorder of Consciousness (pDoC). Occurrence of frontally-oriented microstates (C microstate), likelihood of maintaining such brain state or transitioning to the C topography and complexity were found to be indicators of consciousness presence and levels. Features of left-right asymmetric microstates and transitions toward them were found to be negatively correlated with antero-posterior brain reorganization and EEG symmetry. Substantial differences in microstates' sequence complexity and presence of C topography were found between groups of patients with alpha dominant background, cortical reactivity and antero-posterior gradient. Also, transitioning from left-right to antero-posterior microstates was found to be an independent predictor of consciousness recovery, stronger than consciousness levels at IRU's admission. In conclusions, global brain dynamics measured with scale-free estimators can be considered an indicator of consciousness presence and a candidate marker of short-term recovery in patients with a pDoC.
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Affiliation(s)
- Piergiuseppe Liuzzi
- IRCCS Don Carlo Gnocchi ONLUS, Firenze, Italy; Istituto di BioRobotica, Scuola Superiore Sant'Anna, Pontedera, Italy
| | | | | | | | | | | | | | | | - Francesca Cecchi
- IRCCS Don Carlo Gnocchi ONLUS, Firenze, Italy; Dipartimento di Medicina Sperimentale e Clinica, Università di Firenze, Firenze, Italy
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Jiang M, Niu Z, Liu G, Huang H, Li X, Su Y. Quantitative EEG and brain network analysis: predicting awakening from early coma after cardiopulmonary resuscitation. Neurol Res 2023; 45:969-978. [PMID: 37643397 DOI: 10.1080/01616412.2023.2252281] [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: 03/12/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
OBJECTIVE For patients in early coma after cardiopulmonary resuscitation (CPR), quantitative electroencephalogram (EEG) and brain network analysis was performed to identify relevant indicators of awakening. METHODS A prospective cohort study was conducted on comatose patients after CPR in the neuro-critical care unit. The included patients received clinical evaluation. The bedside high-density (64-lead) EEG monitoring was performed for visual grading and calculation of power spectrum and brain network parameters. A 3-month prognostic assessment was performed and the patients were dichotomized into the awakening group and the unawakening group. RESULTS A total of 25 patients were included. The awakening group had higher GCS score, more slow wave pattern and reactive EEG than the unawakening group (P = 0.003, P < 0.001, P < 0.001, respectively). Compared with the unawakening group, (1) the awakening group had significantly higher absolute and relative θ power and slow/fast band ratio of the whole brain (P < 0.05), (2) the awakening group had stronger connection based on coherence, phase synchronization, phase lag index and cross-correlation (P < 0.05), (3) the awakening group had higher small-worldness, clustering coefficient and average path length based on graph theory (P < 0.05). CONCLUSIONS The power spectrum and brain network characteristics in patients in early coma after CPR have predictive value for recovery.
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Affiliation(s)
- Mengdi Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Currently working at Department of Neurology, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Zikang Niu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern, Beijing Normal University, Beijing, China
| | - Gang Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Huijin Huang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern, Beijing Normal University, Beijing, China
| | - Yingying Su
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
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Panda R, Vanhaudenhuyse A, Piarulli A, Annen J, Demertzi A, Alnagger N, Chennu S, Laureys S, Faymonville ME, Gosseries O. Altered Brain Connectivity and Network Topological Organization in a Non-ordinary State of Consciousness Induced by Hypnosis. J Cogn Neurosci 2023; 35:1394-1409. [PMID: 37315333 DOI: 10.1162/jocn_a_02019] [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: 06/16/2023]
Abstract
Hypnosis has been shown to be of clinical utility; however, its underlying neural mechanisms remain unclear. This study aims to investigate altered brain dynamics during the non-ordinary state of consciousness induced by hypnosis. We studied high-density EEG in 9 healthy participants during eyes-closed wakefulness and during hypnosis, induced by a muscle relaxation and eyes fixation procedure. Using hypotheses based on internal and external awareness brain networks, we assessed region-wise brain connectivity between six ROIs (right and left frontal, right and left parietal, upper and lower midline regions) at the scalp level and compared across conditions. Data-driven, graph-theory analyses were also carried out to characterize brain network topology in terms of brain network segregation and integration. During hypnosis, we observed (1) increased delta connectivity between left and right frontal, as well as between right frontal and parietal regions; (2) decreased connectivity for alpha (between right frontal and parietal and between upper and lower midline regions) and beta-2 bands (between upper midline and right frontal, frontal and parietal, also between upper and lower midline regions); and (3) increased network segregation (short-range connections) in delta and alpha bands, and increased integration (long-range connections) in beta-2 band. This higher network integration and segregation was measured bilaterally in frontal and right parietal electrodes, which were identified as central hub regions during hypnosis. This modified connectivity and increased network integration-segregation properties suggest a modification of the internal and external awareness brain networks that may reflect efficient cognitive-processing and lower incidences of mind-wandering during hypnosis.
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Affiliation(s)
| | | | | | - Jitka Annen
- University of Liège, Belgium
- University Hospital of Liège, Belgium
| | | | - Naji Alnagger
- University of Liège, Belgium
- University Hospital of Liège, Belgium
| | | | - Steven Laureys
- University of Liège, Belgium
- University Hospital of Liège, Belgium
- Laval University, Québec, Canada
| | | | - Olivia Gosseries
- University of Liège, Belgium
- University Hospital of Liège, Belgium
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11
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Annen J, Frasso G, van der Lande GJM, Bonin EAC, Vitello MM, Panda R, Sala A, Cavaliere C, Raimondo F, Bahri MA, Schiff ND, Gosseries O, Thibaut A, Laureys S. Cerebral electrometabolic coupling in disordered and normal states of consciousness. Cell Rep 2023; 42:112854. [PMID: 37498745 DOI: 10.1016/j.celrep.2023.112854] [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: 10/19/2022] [Revised: 06/02/2023] [Accepted: 07/08/2023] [Indexed: 07/29/2023] Open
Abstract
We assess cerebral integrity with cortical and subcortical FDG-PET and cortical electroencephalography (EEG) within the mesocircuit model framework in patients with disorders of consciousness (DoCs). The mesocircuit hypothesis proposes that subcortical activation facilitates cortical function. We find that the metabolic balance of subcortical mesocircuit areas is informative for diagnosis and is associated with four EEG-based power spectral density patterns, cortical metabolism, and α power in healthy controls and patients with a DoC. Last, regional electrometabolic coupling at the cortical level can be identified in the θ and α ranges, showing positive and negative relations with glucose uptake, respectively. This relation is inverted in patients with a DoC, potentially related to altered orchestration of neural activity, and may underlie suboptimal excitability states in patients with a DoC. By understanding the neurobiological basis of the pathophysiology underlying DoCs, we foresee translational value for diagnosis and treatment of patients with a DoC.
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Affiliation(s)
- Jitka Annen
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium.
| | | | - Glenn J M van der Lande
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Estelle A C Bonin
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Marie M Vitello
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Rajanikant Panda
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Arianna Sala
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | | | - Federico Raimondo
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | | | - Olivia Gosseries
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium; Centre du Cerveau(2), University Hospital of Liège, Liège, Belgium; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, University Laval, Quebec City, QC, Canada
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12
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Liuzzi P, Hakiki B, Draghi F, Romoli AM, Burali R, Scarpino M, Cecchi F, Grippo A, Mannini A. EEG fractal dimensions predict high-level behavioral responses in minimally conscious patients. J Neural Eng 2023; 20:046038. [PMID: 37494926 DOI: 10.1088/1741-2552/aceaac] [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: 06/14/2023] [Accepted: 07/26/2023] [Indexed: 07/28/2023]
Abstract
Objective.Brain-injured patients may enter a state of minimal or inconsistent awareness termed minimally conscious state (MCS). Such patient may (MCS+) or may not (MCS-) exhibit high-level behavioral responses, and the two groups retain two inherently different rehabilitative paths and expected outcomes. We hypothesized that brain complexity may be treated as a proxy of high-level cognition and thus could be used as a neural correlate of consciousness.Approach.In this prospective observational study, 68 MCS patients (MCS-: 30; women: 31) were included (median [IQR] age 69 [20]; time post-onset 83 [28]). At admission to intensive rehabilitation, 30 min resting-state closed-eyes recordings were performed together with consciousness diagnosis following international guidelines. The width of the multifractal singularity spectrum (MSS) was computed for each channel time series and entered nested cross-validated interpretable machine learning models targeting the differential diagnosis of MCS±.Main results.Frontal MSS widths (p< 0.05), as well as the ones deriving from the left centro-temporal network (C3:p= 0.018, T3:p= 0.017; T5:p= 0.003) were found to be significantly higher in the MCS+ cohort. The best performing solution was found to be the K-nearest neighbor model with an aggregated test accuracy of 75.5% (median [IQR] AuROC for 100 executions 0.88 [0.02]). Coherently, the electrodes with highest Shapley values were found to be Fz and Cz, with four out the first five ranked features belonging to the fronto-central network.Significance.MCS+ is a frequent condition associated with a notably better prognosis than the MCS-. High fractality in the left centro-temporal network results coherent with neurological networks involved in the language function, proper of MCS+ patients. Using EEG-based interpretable algorithm to complement differential diagnosis of consciousness may improve rehabilitation pathways and communications with caregivers.
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Affiliation(s)
- Piergiuseppe Liuzzi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
- The Biorobotics Institute, Scuola Superiore Sant'Anna Istituto di BioRobotica, Viale Rinaldo Piaggio 34, Pontedera, PI, Italy
| | - Bahia Hakiki
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Francesca Draghi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Anna Maria Romoli
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Rachele Burali
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Maenia Scarpino
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Francesca Cecchi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, Florence, 50143 FI, Italy
| | - Antonello Grippo
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Andrea Mannini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
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Liuzzi P, Hakiki B, Scarpino M, Burali R, Maiorelli A, Draghi F, Romoli AM, Grippo A, Cecchi F, Mannini A. Neural coding of autonomic functions in different states of consciousness. J Neuroeng Rehabil 2023; 20:96. [PMID: 37491259 PMCID: PMC10369699 DOI: 10.1186/s12984-023-01216-6] [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: 03/17/2023] [Accepted: 07/10/2023] [Indexed: 07/27/2023] Open
Abstract
Detecting signs of residual neural activity in patients with altered states of consciousness is a crucial issue for the customization of neurorehabilitation treatments and clinical decision-making. With this large observational prospective study, we propose an innovative approach to detect residual signs of consciousness via the assessment of the amount of autonomic information coded within the brain. The latter was estimated by computing the mutual information (MI) between preprocessed EEG and ECG signals, to be then compared across consciousness groups, together with the absolute power and an international qualitative labeling. One-hundred seventy-four patients (73 females, 42%) were included in the study (median age of 65 years [IQR = 20], MCS +: 29, MCS -: 23, UWS: 29). Electroencephalography (EEG) information content was found to be mostly related to the coding of electrocardiography (ECG) activity, i.e., with higher MI (p < 0.05), in Unresponsive Wakefulness Syndrome and Minimally Consciousness State minus (MCS -). EEG-ECG MI, besides clearly discriminating patients in an MCS - and +, significantly differed between lesioned areas (sides) in a subgroup of unilateral hemorrhagic patients. Crucially, such an accessible and non-invasive measure of residual consciousness signs was robust across electrodes and patient groups. Consequently, exiting from a strictly neuro-centric consciousness detection approach may be the key to provide complementary insights for the objective assessment of patients' consciousness levels and for the patient-specific planning of rehabilitative interventions.
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Affiliation(s)
- Piergiuseppe Liuzzi
- Sant’Anna School of Advanced Studies, The BioRobotics Institute, Viale Rinaldo Piaggio 69, 56025 Pontedera, PI Italy
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, FI 50143 Florence, Italy
| | - Bahia Hakiki
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, FI 50143 Florence, Italy
| | - Maenia Scarpino
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, FI 50143 Florence, Italy
| | - Rachele Burali
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, FI 50143 Florence, Italy
| | - Antonio Maiorelli
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, FI 50143 Florence, Italy
| | - Francesca Draghi
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, FI 50143 Florence, Italy
| | - Anna Maria Romoli
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, FI 50143 Florence, Italy
| | - Antonello Grippo
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, FI 50143 Florence, Italy
| | - Francesca Cecchi
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, FI 50143 Florence, Italy
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50143 Florence, FI Italy
| | - Andrea Mannini
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, FI 50143 Florence, Italy
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14
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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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15
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Yang Y, Dai Y, He Q, Wang S, Chen X, Geng X, He J, Duan F. Altered brain functional connectivity in vegetative state and minimally conscious state. Front Aging Neurosci 2023; 15:1213904. [PMID: 37469954 PMCID: PMC10352323 DOI: 10.3389/fnagi.2023.1213904] [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: 04/28/2023] [Accepted: 06/06/2023] [Indexed: 07/21/2023] Open
Abstract
Objectives The pathological mechanism for a disorder of consciousness (DoC) is still not fully understood. Based on traditional behavioral scales, there is a high rate of misdiagnosis for subtypes of DoC. We aimed to explore whether topological characterization may explain the pathological mechanisms of DoC and be effective in diagnosing the subtypes of DoC. Methods Using resting-state functional magnetic resonance imaging data, the weighted brain functional networks for normal control subjects and patients with vegetative state (VS) and minimally conscious state (MCS) were constructed. Global and local network characteristics of each group were analyzed. A support vector machine was employed to identify MCS and VS patients. Results The average connection strength was reduced in DoC patients and roughly equivalent in MCS and VS groups. Global efficiency, local efficiency, and clustering coefficients were reduced, and characteristic path length was increased in DoC patients (p < 0.05). For patients of both groups, global network measures were not significantly different (p > 0.05). Nodal efficiency, nodal local efficiency, and nodal clustering coefficient were reduced in frontoparietal brain areas, limbic structures, and occipital and temporal brain areas (p < 0.05). The comparison of nodal centrality suggested that DoC causes reorganization of the network structure on a large scale, especially the thalamus. Lobal network measures emphasized that the differences between the two groups of patients mainly involved frontoparietal brain areas. The accuracy, sensitivity, and specificity of the classifier for identifying MCS and VS patients were 89.83, 78.95, and 95%, respectively. Conclusion There is an association between altered network structures and clinical symptoms of DoC. With the help of network metrics, it is feasible to differentiate MCS and VS patients.
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Affiliation(s)
- Yi Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
- Beijing Institute of Brain Disorders, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yangyang Dai
- Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, College of Artificial Intelligence, Nankai University, Tianjin, China
| | - Qiheng He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shan Wang
- Department of Information and Communications Engineering, School of Engineering, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan
| | - Xueling Chen
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoli Geng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianghong He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Feng Duan
- Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation, College of Artificial Intelligence, Nankai University, Tianjin, China
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16
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Koirala N, Deroche MLD, Wolfe J, Neumann S, Bien AG, Doan D, Goldbeck M, Muthuraman M, Gracco VL. Dynamic networks differentiate the language ability of children with cochlear implants. Front Neurosci 2023; 17:1141886. [PMID: 37409105 PMCID: PMC10318154 DOI: 10.3389/fnins.2023.1141886] [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: 01/10/2023] [Accepted: 05/29/2023] [Indexed: 07/07/2023] Open
Abstract
Background Cochlear implantation (CI) in prelingually deafened children has been shown to be an effective intervention for developing language and reading skill. However, there is a substantial proportion of the children receiving CI who struggle with language and reading. The current study-one of the first to implement electrical source imaging in CI population was designed to identify the neural underpinnings in two groups of CI children with good and poor language and reading skill. Methods Data using high density electroencephalography (EEG) under a resting state condition was obtained from 75 children, 50 with CIs having good (HL) or poor language skills (LL) and 25 normal hearing (NH) children. We identified coherent sources using dynamic imaging of coherent sources (DICS) and their effective connectivity computing time-frequency causality estimation based on temporal partial directed coherence (TPDC) in the two CI groups compared to a cohort of age and gender matched NH children. Findings Sources with higher coherence amplitude were observed in three frequency bands (alpha, beta and gamma) for the CI groups when compared to normal hearing children. The two groups of CI children with good (HL) and poor (LL) language ability exhibited not only different cortical and subcortical source profiles but also distinct effective connectivity between them. Additionally, a support vector machine (SVM) algorithm using these sources and their connectivity patterns for each CI group across the three frequency bands was able to predict the language and reading scores with high accuracy. Interpretation Increased coherence in the CI groups suggest overall that the oscillatory activity in some brain areas become more strongly coupled compared to the NH group. Moreover, the different sources and their connectivity patterns and their association to language and reading skill in both groups, suggest a compensatory adaptation that either facilitated or impeded language and reading development. The neural differences in the two groups of CI children may reflect potential biomarkers for predicting outcome success in CI children.
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Affiliation(s)
- Nabin Koirala
- Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, United States
| | | | - Jace Wolfe
- Hearts for Hearing Foundation, Oklahoma City, OK, United States
| | - Sara Neumann
- Hearts for Hearing Foundation, Oklahoma City, OK, United States
| | - Alexander G. Bien
- Department of Otolaryngology – Head and Neck Surgery, University of Oklahoma Medical Center, Oklahoma City, OK, United States
| | - Derek Doan
- University of Oklahoma College of Medicine, Oklahoma City, OK, United States
| | - Michael Goldbeck
- University of Oklahoma College of Medicine, Oklahoma City, OK, United States
| | - Muthuraman Muthuraman
- Department of Neurology, Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI), Universitätsklinikum Würzburg, Würzburg, Germany
| | - Vincent L. Gracco
- Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, United States
- School of Communication Sciences and Disorders, McGill University, Montreal, QC, Canada
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G-Guzmán E, Perl YS, Vohryzek J, Escrichs A, Manasova D, Türker B, Tagliazucchi E, Kringelbach M, Sitt JD, Deco G. The lack of temporal brain dynamics asymmetry as a signature of impaired consciousness states. Interface Focus 2023; 13:20220086. [PMID: 37065259 PMCID: PMC10102727 DOI: 10.1098/rsfs.2022.0086] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/17/2023] [Indexed: 04/18/2023] Open
Abstract
Life is a constant battle against equilibrium. From the cellular level to the macroscopic scale, living organisms as dissipative systems require the violation of their detailed balance, i.e. metabolic enzymatic reactions, in order to survive. We present a framework based on temporal asymmetry as a measure of non-equilibrium. By means of statistical physics, it was discovered that temporal asymmetries establish an arrow of time useful for assessing the reversibility in human brain time series. Previous studies in human and non-human primates have shown that decreased consciousness states such as sleep and anaesthesia result in brain dynamics closer to the equilibrium. Furthermore, there is growing interest in the analysis of brain symmetry based on neuroimaging recordings and since it is a non-invasive technique, it can be extended to different brain imaging modalities and applied at different temporo-spatial scales. In the present study, we provide a detailed description of our methodological approach, paying special attention to the theories that motivated this work. We test, for the first time, the reversibility analysis in human functional magnetic resonance imaging data in patients suffering from disorder of consciousness. We verify that the tendency of a decrease in the asymmetry of the brain signal together with the decrease in non-stationarity are key characteristics of impaired consciousness states. We expect that this work will open the way for assessing biomarkers for patients' improvement and classification, as well as motivating further research on the mechanistic understanding underlying states of impaired consciousness.
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Affiliation(s)
- Elvira G-Guzmán
- Department of Information and Communication Technologies, Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Yonatan Sanz Perl
- Department of Information and Communication Technologies, Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm Physiological Investigation of Clinically Normal and Impaired Cognition Team, CNRS, 75013, Paris, France
| | - Jakub Vohryzek
- Department of Information and Communication Technologies, Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Anira Escrichs
- Department of Information and Communication Technologies, Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Dragana Manasova
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm Physiological Investigation of Clinically Normal and Impaired Cognition Team, CNRS, 75013, Paris, France
- Université Paris Cité, Paris, France
| | - Başak Türker
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm Physiological Investigation of Clinically Normal and Impaired Cognition Team, CNRS, 75013, Paris, France
| | - Enzo Tagliazucchi
- Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires, Argentina
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Morten Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Department of Clinical Medicine, Center for Music in the Brain, Aarhus University, Jutland, Denmark
| | - Jacobo D. Sitt
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm Physiological Investigation of Clinically Normal and Impaired Cognition Team, CNRS, 75013, Paris, France
| | - Gustavo Deco
- Department of Information and Communication Technologies, Centre for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
- Department of Neuropsychology, Max Planck Institute for human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne, Australia
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18
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Nguyen KH, Ebbatson M, Tran Y, Craig A, Nguyen H, Chai R. Source-Space Brain Functional Connectivity Features in Electroencephalogram-Based Driver Fatigue Classification. SENSORS (BASEL, SWITZERLAND) 2023; 23:2383. [PMID: 36904587 PMCID: PMC10007183 DOI: 10.3390/s23052383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/10/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.
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Affiliation(s)
- Khanh Ha Nguyen
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Matthew Ebbatson
- School of Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Yvonne Tran
- Department of Linguistics, Macquarie University Hearing, Macquarie University, Sydney, NSW 2109, Australia
| | - Ashley Craig
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
- John Walsh Centre for Rehabilitation Research, Kolling Institute, Northern Sydney Local Health District, St Leonards, Sydney, NSW 2065, Australia
| | - Hung Nguyen
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Rifai Chai
- School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
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19
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Ballanti S, Campagnini S, Liuzzi P, Hakiki B, Scarpino M, Macchi C, Oddo CM, Carrozza MC, Grippo A, Mannini A. EEG-based methods for recovery prognosis of patients with disorders of consciousness: A systematic review. Clin Neurophysiol 2022; 144:98-114. [PMID: 36335795 DOI: 10.1016/j.clinph.2022.09.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 09/15/2022] [Accepted: 09/19/2022] [Indexed: 11/19/2022]
Abstract
OBJECTIVE Disorders of consciousness (DoC) are acquired conditions of severely altered consciousness. Electroencephalography (EEG)-derived biomarkers have been studied as clinical predictors of consciousness recovery. Therefore, this study aimed to systematically review the methods, features, and models used to derive prognostic EEG markers in patients with DoC in a rehabilitation setting. METHODS We conducted a systematic literature search of EEG-based strategies for consciousness recovery prognosis in five electronic databases. RESULTS The search resulted in 2964 papers. After screening, 15 studies were included in the review. Our analyses revealed that simpler experimental settings and similar filtering cut-off frequencies are preferred. The results of studies were categorised by extracting qualitative and quantitative features. The quantitative features were further classified into evoked/event-related potentials, spectral measures, entropy measures, and graph-theory measures. Despite the variety of methods, features from all categories, including qualitative ones, exhibited significant correlations with DoC prognosis. Moreover, no agreement was found on the optimal set of EEG-based features for the multivariate prognosis of patients with DoC, which limits the computational methods applied for outcome prediction and correlation analysis to classical ones. Nevertheless, alpha power, reactivity, and higher complexity metrics were often found to be predictive of consciousness recovery. CONCLUSIONS This study's findings confirm the essential role of qualitative EEG and suggest an important role for quantitative EEG. Their joint use could compensate for their reciprocal limitations. SIGNIFICANCE This study emphasises the need for further efforts toward guidelines on standardised EEG analysis pipeline, given the already proven role of EEG markers in the recovery prognosis of patients with DoC.
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Affiliation(s)
- Sara Ballanti
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy; The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera 56025, Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa 56127, Italy.
| | - Silvia Campagnini
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy; The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera 56025, Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa 56127, Italy.
| | - Piergiuseppe Liuzzi
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy; The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera 56025, Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa 56127, Italy.
| | - Bahia Hakiki
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy.
| | | | - Claudio Macchi
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy; Department of Experimental and Clinical Medicine, University of Florence, Firenze 50143, Italy.
| | - Calogero Maria Oddo
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera 56025, Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa 56127, Italy.
| | - Maria Chiara Carrozza
- The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera 56025, Pisa, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa 56127, Italy.
| | | | - Andrea Mannini
- IRCCS Fondazione Don Carlo Gnocchi, Firenze 50143, Italy.
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20
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Wang H, Zhang Y, Cheng H, Yan F, Song D, Wang Q, Cai S, Wang Y, Huang L. Selective corticocortical connectivity suppression during propofol-induced anesthesia in healthy volunteers. Cogn Neurodyn 2022; 16:1029-1043. [PMID: 36237410 PMCID: PMC9508318 DOI: 10.1007/s11571-021-09775-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 11/17/2021] [Accepted: 12/13/2021] [Indexed: 11/03/2022] Open
Abstract
We comprehensively studied directional feedback and feedforward connectivity to explore potential connectivity changes that underlie propofol-induced deep sedation. We further investigated the corticocortical connectivity patterns within and between hemispheres. Sixty-channel electroencephalographic data were collected from 19 healthy volunteers in a resting wakefulness state and propofol-induced deep unconsciousness state defined by a bispectral index value of 40. A source analysis was employed to locate cortical activity. The Desikan-Killiany atlas was used to partition cortices, and directional functional connectivity was assessed by normalized symbolic transfer entropy between higher-order (prefrontal and frontal) and lower-order (auditory, sensorimotor and visual) cortices and between hot-spot frontal and parietal cortices. We found that propofol significantly suppressed feedforward connectivity from the left parietal to right frontal cortex and bidirectional connectivity between the left frontal and left parietal cortex, between the frontal and auditory cortex, and between the frontal and sensorimotor cortex. However, there were no significant changes in either feedforward or feedback connectivity between the prefrontal and all the lower-order cortices and between the frontal and visual cortices or in feedback connectivity from the frontal to parietal cortex. Propofol anesthetic selectively decreased the unidirectional interaction between higher-order frontoparietal cortices and bidirectional interactions between the higher-order frontal cortex and lower-order auditory and sensorimotor cortices, which indicated that both feedback and feedforward connectivity were suppressed under propofol-induced deep sedation. Our findings provide critical insights into the connectivity changes underlying the top-down mechanism of propofol anesthesia at deep sedation. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-021-09775-x.
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Affiliation(s)
- Haidong Wang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Yun Zhang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Huanhuan Cheng
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Fei Yan
- Department of Anesthesiology & Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, 710061 China
| | - Dawei Song
- Department of Anesthesiology & Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, 710061 China
| | - Qiang Wang
- Department of Anesthesiology & Center for Brain Science, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Road, Xi’an, 710061 China
| | - Suping Cai
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Yubo Wang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, No. 2 South Taibai Road, Xi’an, 710071 China
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21
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Panda R, Thibaut A, Lopez-Gonzalez A, Escrichs A, Bahri MA, Hillebrand A, Deco G, Laureys S, Gosseries O, Annen J, Tewarie P. Disruption in structural-functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness. eLife 2022; 11:77462. [PMID: 35916363 PMCID: PMC9385205 DOI: 10.7554/elife.77462] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace (GNW) and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, the prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, that is so-called eigenmodes, was supplemented as potential markers. By analysing time-resolved functional connectivity from functional MRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and subcortical fronto-temporoparietal network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the GNW theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.
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Affiliation(s)
| | | | - Ane Lopez-Gonzalez
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Mohamed Ali Bahri
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gustavo Deco
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | | | | | - Jitka Annen
- Coma Science Group, University of Liège, Liège, Belgium
| | - Prejaas Tewarie
- School of Physics, University of Nottingham, Nottingham, United Kingdom
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22
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Helmstaedter C, Rings T, Buscher L, Janssen B, Alaeddin S, Krause V, Knecht S, Lehnertz K. Stimulation-related modifications of evolving functional brain networks in unresponsive wakefulness. Sci Rep 2022; 12:11586. [PMID: 35803974 PMCID: PMC9270393 DOI: 10.1038/s41598-022-15803-5] [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: 12/14/2021] [Accepted: 06/29/2022] [Indexed: 11/09/2022] Open
Abstract
Recent advances in neurophysiological brain network analysis have demonstrated novel potential for diagnosis and prognosis of disorders of consciousness. While most progress has been achieved on the population-sample level, time-economic and easy-to-apply personalized solutions are missing. This prospective controlled study combined EEG recordings, basal stimulation, and daily behavioral assessment as applied routinely during complex early rehabilitation treatment. We investigated global characteristics of EEG-derived evolving functional brain networks during the repeated (3–6 weeks apart) evaluation of brain dynamics at rest as well as during and after multisensory stimulation in ten patients who were diagnosed with an unresponsive wakefulness syndrome (UWS). The age-corrected average clustering coefficient C* allowed to discriminate between individual patients at first (three patients) and second assessment (all patients). Clinically, only two patients changed from UWS to minimally conscious state. Of note, most patients presented with significant changes of C* due to stimulations, along with treatment, and with an increasing temporal distance to injury. These changes tended towards the levels of nine healthy controls. Our approach allowed to monitor both, short-term effects of individual therapy sessions and possibly long-term recovery. Future studies will need to assess its full potential for disease monitoring and control of individualized treatment decisions.
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Affiliation(s)
- Christoph Helmstaedter
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany. .,Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127, Bonn, Germany.
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115, Bonn, Germany
| | - Lara Buscher
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany
| | - Benedikt Janssen
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany
| | - Sara Alaeddin
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany
| | - Vanessa Krause
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany
| | - Stefan Knecht
- St. Mauritius Therapieklinik GmbH, Strümper Str. 111, 40670, Meerbusch, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127, Bonn, Germany.,Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115, Bonn, Germany.,Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Str. 7, 53175, Bonn, Germany
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23
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Duszyk-Bogorodzka A, Zieleniewska M, Jankowiak-Siuda K. Brain Activity Characteristics of Patients With Disorders of Consciousness in the EEG Resting State Paradigm: A Review. Front Syst Neurosci 2022; 16:654541. [PMID: 35720438 PMCID: PMC9198636 DOI: 10.3389/fnsys.2022.654541] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
The assessment of the level of consciousness in disorders of consciousness (DoC) is still one of the most challenging problems in contemporary medicine. Nevertheless, based on the multitude of studies conducted over the last 20 years on resting states based on electroencephalography (EEG) in DoC, it is possible to outline the brain activity profiles related to both patients without preserved consciousness and minimally conscious ones. In the case of patients without preserved consciousness, the dominance of low, mostly delta, frequency, and the marginalization of the higher frequencies were observed, both in terms of the global power of brain activity and in functional connectivity patterns. In turn, the minimally conscious patients revealed the opposite brain activity pattern—the characteristics of higher frequency bands were preserved both in global power and in functional long-distance connections. In this short review, we summarize the state of the art of EEG-based research in the resting state paradigm, in the context of providing potential support to the traditional clinical assessment of the level of consciousness.
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Affiliation(s)
- Anna Duszyk-Bogorodzka
- Behavioural Neuroscience Lab, Institute of Psychology, SWPS University of Social Sciences and Humanities, Warsaw, Poland
- *Correspondence: Anna Duszyk-Bogorodzka
| | | | - Kamila Jankowiak-Siuda
- Behavioural Neuroscience Lab, Institute of Psychology, SWPS University of Social Sciences and Humanities, Warsaw, Poland
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24
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Pepperell R. Does Machine Understanding Require Consciousness? Front Syst Neurosci 2022; 16:788486. [PMID: 35664685 PMCID: PMC9159796 DOI: 10.3389/fnsys.2022.788486] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 04/12/2022] [Indexed: 11/24/2022] Open
Abstract
This article addresses the question of whether machine understanding requires consciousness. Some researchers in the field of machine understanding have argued that it is not necessary for computers to be conscious as long as they can match or exceed human performance in certain tasks. But despite the remarkable recent success of machine learning systems in areas such as natural language processing and image classification, important questions remain about their limited performance and about whether their cognitive abilities entail genuine understanding or are the product of spurious correlations. Here I draw a distinction between natural, artificial, and machine understanding. I analyse some concrete examples of natural understanding and show that although it shares properties with the artificial understanding implemented in current machine learning systems it also has some essential differences, the main one being that natural understanding in humans entails consciousness. Moreover, evidence from psychology and neurobiology suggests that it is this capacity for consciousness that, in part at least, explains for the superior performance of humans in some cognitive tasks and may also account for the authenticity of semantic processing that seems to be the hallmark of natural understanding. I propose a hypothesis that might help to explain why consciousness is important to understanding. In closing, I suggest that progress toward implementing human-like understanding in machines—machine understanding—may benefit from a naturalistic approach in which natural processes are modelled as closely as possible in mechanical substrates.
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25
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Zhuang W, Wang J, Chu C, Wei X, Yi G, Dong Y, Cai L. Disrupted Control Architecture of Brain Network in Disorder of Consciousness. IEEE Trans Neural Syst Rehabil Eng 2022; 30:400-409. [PMID: 35143400 DOI: 10.1109/tnsre.2022.3150834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The human brain controls various cognitive functions via the functional coordination of multiple brain regions in an efficient and robust way. However, the relationship between consciousness state and the control mode of brain networks is poorly explored. Using multi-channel EEG, the present study aimed to characterize the abnormal control architecture of functional brain networks in the patients with disorders of consciousness (DOC). Resting state EEG data were collected from 40 DOC patients with different consciousness levels and 24 healthy subjects. Functional brain networks were constructed in five different EEG frequency bands and the broadband in the source level. Subsequently, a control architecture framework based on the minimum dominating set was applied to investigate the of control mode of functional brain networks for the subjects with different conscious states. Results showed that regardless of the consciousness levels, the functional networks of human brain operate in a distributed and overlapping control architecture different from that of random networks. Compared to the healthy controls, the patients have a higher control cost manifested by more minimum dominating nodes and increased degree of distributed control, especially in the alpha band. The ability to withstand network attack for the control architecture is positive correlated with the consciousness levels. The distributed of control increased correlation levels with Coma Recovery Scale-Revised score and improved separation between unresponsive wakefulness syndrome and minimal consciousness state. These findings may benefit our understanding of consciousness and provide potential biomarkers for the assessment of consciousness levels.
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26
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A survey of brain network analysis by electroencephalographic signals. Cogn Neurodyn 2022; 16:17-41. [PMID: 35126769 PMCID: PMC8807775 DOI: 10.1007/s11571-021-09689-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/25/2021] [Accepted: 05/31/2021] [Indexed: 02/03/2023] Open
Abstract
Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.
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27
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Liu Y, Li Z, Bai Y. Frontal and parietal lobes play crucial roles in understanding the disorder of consciousness: A perspective from electroencephalogram studies. Front Neurosci 2022; 16:1024278. [PMID: 36778900 PMCID: PMC9909102 DOI: 10.3389/fnins.2022.1024278] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 12/19/2022] [Indexed: 01/27/2023] Open
Abstract
Background Electroencephalogram (EEG) studies have established many characteristics relevant to consciousness levels of patients with disorder of consciousness (DOC). Although the frontal and parietal brain regions were often highlighted in DOC studies, their electro-neurophysiological roles in constructing human consciousness remain unclear because of the fragmented information from literatures and the complexity of EEG characteristics. Methods Existing EEG studies of DOC patients were reviewed and summarized. Relevant findings and results about the frontal and parietal regions were filtered, compared, and concluded to clarify their roles in consciousness classification and outcomes. The evidence covers multi-dimensional EEG characteristics including functional connectivity, non-linear dynamics, spectrum power, transcranial magnetic stimulation-electroencephalography (TMS-EEG), and event-related potential. Results and conclusion Electroencephalogram characteristics related to frontal and parietal regions consistently showed high relevance with consciousness: enhancement of low-frequency rhythms, suppression of high-frequency rhythms, reduction of dynamic complexity, and breakdown of networks accompanied with decreasing consciousness. Owing to the limitations of EEG, existing studies have not yet clarified which one between the frontal and parietal has priority in consciousness injury or recovery. Source reconstruction with high-density EEG, machine learning with large samples, and TMS-EEG mapping will be important approaches for refining EEG awareness locations.
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Affiliation(s)
- Yesong Liu
- School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Zhaoyi Li
- School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Yang Bai
- School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
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28
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López-González A, Panda R, Ponce-Alvarez A, Zamora-López G, Escrichs A, Martial C, Thibaut A, Gosseries O, Kringelbach ML, Annen J, Laureys S, Deco G. Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics. Commun Biol 2021; 4:1037. [PMID: 34489535 PMCID: PMC8421429 DOI: 10.1038/s42003-021-02537-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 08/11/2021] [Indexed: 01/07/2023] Open
Abstract
Low-level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol-induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low-level states of consciousness, thus attenuating the differences between hubs and non-hubs brain dynamics. López-González et al study the fMRI brain dynamics and their underlying mechanism from patients that suffered brain injuries leading to a disorder of consciousness as well as from healthy subjects undergoing propofol-induced sedation. They show that pathological and pharmacological low-level states of consciousness display disrupted synchronization patterns, higher constraint to the anatomy and a loss of heterogeneity and stability in the structural hubs compared to conscious states.
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Affiliation(s)
- Ane López-González
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Rajanikant Panda
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Adrián Ponce-Alvarez
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gorka Zamora-López
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Charlotte Martial
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Olivia Gosseries
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK.,Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus C, Denmark.,Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
| | - Jitka Annen
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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29
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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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30
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Marimpis AD, Dimitriadis SI, Goebel R. Dyconnmap: Dynamic connectome mapping-A neuroimaging python module. Hum Brain Mapp 2021; 42:4909-4939. [PMID: 34250674 PMCID: PMC8449119 DOI: 10.1002/hbm.25589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 06/10/2021] [Accepted: 06/25/2021] [Indexed: 11/16/2022] Open
Abstract
Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time‐averaged approaches and sophisticated algorithms that can capture the time‐varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word ‘chronnectome’ (integration of the Greek word ‘Chronos’, which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex human mind. The temporal evolution of these brain states and well‐known network metrics could give rise to new analytic trends. Functional brain networks could also increase the multi‐faced nature of the dynamic networks revealing complementary information. Finally, we describe a python module (https://github.com/makism/dyconnmap) which accompanies this article and contains a collection of dynamic complex network analytics and measures and demonstrates its great promise for the study of a healthy subject's repeated fMRI scans.
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Affiliation(s)
- Avraam D Marimpis
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Brain Innovation B.V, Maastricht, The Netherlands
| | - Stavros I Dimitriadis
- Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Rainer Goebel
- Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.,Brain Innovation B.V, Maastricht, The Netherlands
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31
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Cheremushkin EA, Petrenko NE, Dorokhov VB. [Sleep and neurophysiological correlates of consciousness activation upon awakening]. Zh Nevrol Psikhiatr Im S S Korsakova 2021; 121:14-18. [PMID: 34078854 DOI: 10.17116/jnevro202112104214] [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: 11/17/2022]
Abstract
The authors discuss modern ideas about the neurophysiological mechanisms of awakening from sleep and the results of own EEG studies of the spatio-temporal dynamics of the activity of the cerebral hemispheres using the own experimental model for studying consciousness in the sleep-wake paradigm. This model is based on continuous execution of a monotonous psychomotor test performed lying down with eyes closed and allows observing several short-term sleep episodes during a 1-hour experiment, followed by spontaneous awakening and restoration of the psychomotor test. A necessary condition for the restoration of activity during spontaneous awakening is the emergence of the EEG alpha rhythm, the parameters of which determine the effectiveness of the restoration of the psychomotor test and, accordingly, the achievement of a certain level of consciousness, and therefore can be considered as a neurophysiological correlate of consciousness activation upon awakening. The considered experimental model of consciousness can be useful for analyzing the neurophysiological mechanisms of consciousness activation in patients with chronic impairments of consciousness and for searching for effective methods for the rehabilitation of such patients.
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Affiliation(s)
- E A Cheremushkin
- Institute of Higher Nervous Activity and Neurophysiology, Moscow, Russia
| | - N E Petrenko
- Institute of Higher Nervous Activity and Neurophysiology, Moscow, Russia
| | - V B Dorokhov
- Institute of Higher Nervous Activity and Neurophysiology, Moscow, Russia
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32
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Wutzl B, Golaszewski SM, Leibnitz K, Langthaler PB, Kunz AB, Leis S, Schwenker K, Thomschewski A, Bergmann J, Trinka E. Narrative Review: Quantitative EEG in Disorders of Consciousness. Brain Sci 2021; 11:brainsci11060697. [PMID: 34070647 PMCID: PMC8228474 DOI: 10.3390/brainsci11060697] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 05/17/2021] [Accepted: 05/21/2021] [Indexed: 02/06/2023] Open
Abstract
In this narrative review, we focus on the role of quantitative EEG technology in the diagnosis and prognosis of patients with unresponsive wakefulness syndrome and minimally conscious state. This paper is divided into two main parts, i.e., diagnosis and prognosis, each consisting of three subsections, namely, (i) resting-state EEG, including spectral power, functional connectivity, dynamic functional connectivity, graph theory, microstates and nonlinear measurements, (ii) sleep patterns, including rapid eye movement (REM) sleep, slow-wave sleep and sleep spindles and (iii) evoked potentials, including the P300, mismatch negativity, the N100, the N400 late positive component and others. Finally, we summarize our findings and conclude that QEEG is a useful tool when it comes to defining the diagnosis and prognosis of DOC patients.
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Affiliation(s)
- Betty Wutzl
- Graduate School of Information Science and Technology, Osaka University, Suita 565-0871, Japan; (B.W.); (K.L.)
- Symbiotic Intelligent Systems Research Center, Osaka University, Suita 565-0871, Japan
| | - Stefan M. Golaszewski
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Karl Landsteiner Institute for Neurorehabilitation and Space Neurology, 5020 Salzburg, Austria
- Neuroscience Institute, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Kenji Leibnitz
- Graduate School of Information Science and Technology, Osaka University, Suita 565-0871, Japan; (B.W.); (K.L.)
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita 565-0871, Japan
| | - Patrick B. Langthaler
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Department of Mathematics, Paris Lodron University of Salzburg, 5020 Salzburg, Austria
- Team Biostatistics and Big Medical Data, IDA Lab Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Alexander B. Kunz
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Karl Landsteiner Institute for Neurorehabilitation and Space Neurology, 5020 Salzburg, Austria
| | - Stefan Leis
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Kerstin Schwenker
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Karl Landsteiner Institute for Neurorehabilitation and Space Neurology, 5020 Salzburg, Austria
- Neuroscience Institute, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, 5020 Salzburg, Austria
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Aljoscha Thomschewski
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Neuroscience Institute, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, 5020 Salzburg, Austria
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Jürgen Bergmann
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Neuroscience Institute, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, Affiliated Member of the European Reference Network EpiCARE, 5020 Salzburg, Austria; (S.M.G.); (P.B.L.); (A.B.K.); (S.L.); (K.S.); (A.T.); (J.B.)
- Karl Landsteiner Institute for Neurorehabilitation and Space Neurology, 5020 Salzburg, Austria
- Neuroscience Institute, Christian Doppler Medical Center, and Centre for Cognitive Neuroscience, Paracelsus Medical University, 5020 Salzburg, Austria
- Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, 5020 Salzburg, Austria
- Correspondence: ; Tel.: +43-5-7255-34600
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Do TTN, Jung TP, Lin CT. Retrosplenial Segregation Reflects the Navigation Load During Ambulatory Movement. IEEE Trans Neural Syst Rehabil Eng 2021; 29:488-496. [PMID: 33544675 DOI: 10.1109/tnsre.2021.3057384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Spatial navigation is a complex cognitive process based on vestibular, proprioceptive, and visualcues that are integrated and processed by an extensive network of brain areas. The retrosplenial complex (RSC) is an integral part of coordination and translation between spatial reference frames. Previous studies have demonstrated that the RSC is active during a spatial navigation tasks. The specifics of RSC activity under various navigation loads, however, are still not characterized. This study investigated the local information processed by the RSC under various navigation load conditions manipulated by the number of turns in the physical navigation setup. The results showed that the local information processed via the RSC, which was reflected by the segregation network, was higher when the number of turns increased, suggesting that RSC activity is associated with the navigation task load. The present findings shed light on how the brain processes spatial information in a physical navigation task.
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34
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Naro A, Maggio MG, Leo A, Calabrò RS. Multiplex and Multilayer Network EEG Analyses: A Novel Strategy in the Differential Diagnosis of Patients with Chronic Disorders of Consciousness. Int J Neural Syst 2020; 31:2050052. [PMID: 33034532 DOI: 10.1142/s0129065720500525] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The deterioration of specific topological network measures that quantify different features of whole-brain functional network organization can be considered a marker for awareness impairment. Such topological measures reflect the functional interactions of multiple brain structures, which support the integration of different sensorimotor information subtending awareness. However, conventional, single-layer, graph theoretical analysis (GTA)-based approaches cannot always reliably differentiate patients with Disorders of Consciousness (DoC). Using multiplex and multilayer network analyses of frequency-specific and area-specific networks, we investigated functional connectivity during resting-state EEG in 17 patients with Unresponsive Wakefulness Syndrome (UWS) and 15 with Minimally Conscious State (MCS). Multiplex and multilayer network metrics indicated the deterioration and heterogeneity of functional networks and, particularly, the frontal-parietal (FP), as the discriminant between patients with MCS and UWS. These data were not appreciable when considering each individual frequency-specific network. The distinctive properties of multiplex/multilayer network metrics and individual frequency-specific network metrics further suggest the value of integrating the networks as opposed to analyzing frequency-specific network metrics one at a time. The hub vulnerability of these regions was positively correlated with the behavioral responsiveness, thus strengthening the clinically-based differential diagnosis. Therefore, it may be beneficial to adopt both multiplex and multilayer network analyses when expanding the conventional GTA-based analyses in the differential diagnosis of patients with DoC. Multiplex analysis differentiated patients at a group level, whereas the multilayer analysis offered complementary information to differentiate patients with DoC individually. Although further studies are necessary to confirm our preliminary findings, these results contribute to the issue of DoC differential diagnosis and may help in guiding patient-tailored management.
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Affiliation(s)
- Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
| | - Maria Grazia Maggio
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
| | - Antonino Leo
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy
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35
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Zhang R, Zhang L, Guo Y, Shi L, Gao J, Wang X, Hu Y. Effects of High-Definition Transcranial Direct-Current Stimulation on Resting-State Functional Connectivity in Patients With Disorders of Consciousness. Front Hum Neurosci 2020; 14:560586. [PMID: 33100996 PMCID: PMC7546763 DOI: 10.3389/fnhum.2020.560586] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 08/26/2020] [Indexed: 12/23/2022] Open
Abstract
Recently a positive treatment effect on disorders of consciousness (DOCs) with high-definition transcranial direct-current stimulation (HD-tDCS) has been reported; however, the neural modulation mechanisms of this treatment’s efficacy need further investigation. To better understand the processing of HD-tDCS interventions, a long-lasting HD-tDCS protocol was applied to 15 unresponsive wakefulness syndrome (UWS) patients and 20 minimally conscious states (MCS) patients in this study. Ten minutes of resting-state electroencephalograms (EEGs) were recorded from the patients, and the coma recovery scale-revised scores (CRS-Rs) were assessed for each patient from four time-points (T0, T1, T2, and T3). Brain networks were constructed by calculating the EEG spectral connectivity using the debiased weighted phase lag index (dwPLI) and then quantified the network information transmission efficiency by graph theory. We found that there was an increasing trend in local and global information processing of beta and gamma bands in resting-state functional brain networks during the 14 days of HD-tDCS modulation for MCS patients. Furthermore, the increased functional connectivity not only occurred in the local brain area surrounding the stimulation position but was also present across more global brain areas. Our results suggest that long-lasting HD-tDCS on the precuneus may facilitate information processing among neural populations in MCS patients.
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Affiliation(s)
- Rui Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
| | - Lipeng Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
| | - Yongkun Guo
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Department of Neurosurgery, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, China.,Beijing National Research Center for Information Science and Technology, Beijing, China
| | - Jinfeng Gao
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
| | - Xinjun Wang
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuxia Hu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
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36
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Bai Y, Lin Y, Ziemann U. Managing disorders of consciousness: the role of electroencephalography. J Neurol 2020; 268:4033-4065. [PMID: 32915309 PMCID: PMC8505374 DOI: 10.1007/s00415-020-10095-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 07/18/2020] [Accepted: 07/18/2020] [Indexed: 02/07/2023]
Abstract
Disorders of consciousness (DOC) are an important but still underexplored entity in neurology. Novel electroencephalography (EEG) measures are currently being employed for improving diagnostic classification, estimating prognosis and supporting medicolegal decision-making in DOC patients. However, complex recording protocols, a confusing variety of EEG measures, and complicated analysis algorithms create roadblocks against broad application. We conducted a systematic review based on English-language studies in PubMed, Medline and Web of Science databases. The review structures the available knowledge based on EEG measures and analysis principles, and aims at promoting its translation into clinical management of DOC patients.
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Affiliation(s)
- Yang Bai
- International Vegetative State and Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China
- Department of Neurology and Stroke, University of Tübingen, Hoppe‑Seyler‑Str. 3, 72076, Tübingen, Germany
- Hertie Institute for Clinical Brain Research, University of Tübingen, 72076, Tübingen, Germany
| | - Yajun Lin
- International Vegetative State and Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China
| | - Ulf Ziemann
- Department of Neurology and Stroke, University of Tübingen, Hoppe‑Seyler‑Str. 3, 72076, Tübingen, Germany.
- Hertie Institute for Clinical Brain Research, University of Tübingen, 72076, Tübingen, Germany.
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37
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Nadin D, Duclos C, Mahdid Y, Rokos A, Badawy M, Létourneau J, Arbour C, Plourde G, Blain-Moraes S. Brain network motif topography may predict emergence from disorders of consciousness: a case series. Neurosci Conscious 2020; 2020:niaa017. [PMID: 33376599 PMCID: PMC7751128 DOI: 10.1093/nc/niaa017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 03/18/2020] [Accepted: 06/03/2020] [Indexed: 11/16/2022] Open
Abstract
Neuroimaging methods have improved the accuracy of diagnosis in patients with disorders of consciousness (DOC), but novel, clinically translatable methods for prognosticating this population are still needed. In this case series, we explored the association between topographic and global brain network properties and prognosis in patients with DOC. We recorded high-density electroencephalograms in three patients with acute or chronic DOC, two of whom also underwent an anesthetic protocol. In these two cases, we compared functional network motifs, network hubs and power topography (i.e. topographic network properties), as well as relative power and graph theoretical measures (i.e. global network properties), at baseline, during exposure to anesthesia and after recovery from anesthesia. We also compared these properties to a group of healthy, conscious controls. At baseline, the topographic distribution of nodes participating in alpha motifs resembled conscious controls in patients who later recovered consciousness and high relative power in the delta band was associated with a negative outcome. Strikingly, the reorganization of network motifs, network hubs and power topography under anesthesia followed by their return to a baseline patterns upon recovery from anesthesia, was associated with recovery of consciousness. Our findings suggest that topographic network properties measured at the single-electrode level might provide more prognostic information than global network properties that are averaged across the brain network. In addition, we propose that the brain network's capacity to reorganize in response to a perturbation is a precursor to the recovery of consciousness in DOC patients.
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Affiliation(s)
- Danielle Nadin
- Montreal General Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada
- Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Catherine Duclos
- Montreal General Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada
- School of Physical and Occupational Therapy, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Yacine Mahdid
- Montreal General Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada
- Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Alexander Rokos
- Montreal General Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada
- Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Mohamed Badawy
- Montreal Neurological Hospital and Institute, McGill University Health Center, Montreal, QC, Canada
- Department of Anesthesia, McGill University, Montreal, QC, Canada
| | - Justin Létourneau
- Montreal Neurological Hospital and Institute, McGill University Health Center, Montreal, QC, Canada
- Department of Anesthesia, McGill University, Montreal, QC, Canada
| | - Caroline Arbour
- Centre de recherche, CIUSSS du-Nord-de-l’Île-de-Montréal, Montreal, QC, Canada
- Faculty of Nursing, Université de Montréal, Montreal, QC, Canada
| | - Gilles Plourde
- Montreal Neurological Hospital and Institute, McGill University Health Center, Montreal, QC, Canada
- Department of Anesthesia, McGill University, Montreal, QC, Canada
| | - Stefanie Blain-Moraes
- Montreal General Hospital, McGill University Health Center Research Institute, Montreal, QC, Canada
- School of Physical and Occupational Therapy, Faculty of Medicine, McGill University, Montreal, QC, Canada
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38
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Huang H, Niu Z, Liu G, Jiang M, Jia Q, Li X, Su Y. Early Consciousness Disorder in Acute Large Hemispheric Infarction: An Analysis Based on Quantitative EEG and Brain Network Characteristics. Neurocrit Care 2020; 33:376-388. [PMID: 32705419 DOI: 10.1007/s12028-020-01051-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 07/05/2020] [Indexed: 11/28/2022]
Abstract
BACKGROUND Large hemispheric infarction (LHI) is an ischemic stroke affecting at least two-thirds of the middle cerebral artery territory, with or without involvement of the anterior cerebral artery or posterior cerebral artery, and approximately 77% of LHI patients have early consciousness disorder (ECD). We constructed a functional brain network for LHI patients with an acute consciousness disorder to identify new diagnostic markers related to ECDs by analyzing brain network characteristics and mechanisms. METHODS Between August 1, 2017, and September 30, 2018, patients with acute (< 1 month) LHI were admitted to the neurocritical care unit at Xuanwu Hospital of Capital Medical University. Electroencephalography (EEG) data were recorded, and the MATLAB platform (2017b) was used to calculate spectral power, entropy, coherence and phase synchronization. The quantitative EEG and brain network characteristics of different consciousness states and different frequency bands were analyzed (α = 0.05). EEG data were recorded 38 times in 30 patients, 25 of whom were in the ECD group, while 13 patients were in the conscious group. RESULTS (1) Spectral power analysis: The conscious group had higher beta relative spectral power across the whole brain, higher alpha spectral power in the frontal-parietal lobe on the infarction contralateral side, and lower theta and delta spectral power in the central-occipital lobe on the infarction contralateral side than the ECD group. (2) Entropy analysis: The conscious group had higher approximate entropy (ApEn) and permutation entropy (PeEn) across the whole brain than the ECD group. (3) Coherence: The conscious group had higher alpha coherence in nearly the whole brain and higher beta coherence in the bilateral frontal-parietal and parietal-occipital lobes than the ECD group. (4) Phase synchronization: The conscious group had higher alpha and beta synchronization in nearly the whole brain, particularly in the frontal-parietal and parietal-occipital lobes, than the ECD group. (5) Graph theory: The conscious group had higher small-worldness in each frequency band than the ECD group. CONCLUSION In patients with LHI, higher levels of consciousness were associated with more alpha and beta oscillations and fewer delta and theta oscillations. Higher ApEn, PeEn, total brain connectivity, and small-worldness and a wider signal distribution range corresponded to a higher consciousness level.
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Affiliation(s)
- Huijin Huang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zikang Niu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern, Beijing Normal University, Beijing, China
| | - Gang Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Mengdi Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Qingxia Jia
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern, Beijing Normal University, Beijing, China.
| | - Yingying Su
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
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39
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Mheich A, Wendling F, Hassan M. Brain network similarity: methods and applications. Netw Neurosci 2020; 4:507-527. [PMID: 32885113 PMCID: PMC7462433 DOI: 10.1162/netn_a_00133] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2019] [Accepted: 02/26/2020] [Indexed: 12/11/2022] Open
Abstract
Graph theoretical approach has proved an effective tool to understand, characterize, and quantify the complex brain network. However, much less attention has been paid to methods that quantitatively compare two graphs, a crucial issue in the context of brain networks. Comparing brain networks is indeed mandatory in several network neuroscience applications. Here, we discuss the current state of the art, challenges, and a collection of analysis tools that have been developed in recent years to compare brain networks. We first introduce the graph similarity problem in brain network application. We then describe the methodological background of the available metrics and algorithms of comparing graphs, their strengths, and limitations. We also report results obtained in concrete applications from normal brain networks. More precisely, we show the potential use of brain network similarity to build a “network of networks” that may give new insights into the object categorization in the human brain. Additionally, we discuss future directions in terms of network similarity methods and applications.
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Affiliation(s)
- Ahmad Mheich
- Laboratoire Traitement du Signal et de l'Image, Institut National de la Santé et de la Recherche Médicale, Rennes, France
| | - Fabrice Wendling
- Laboratoire Traitement du Signal et de l'Image, Institut National de la Santé et de la Recherche Médicale, Rennes, France
| | - Mahmoud Hassan
- Laboratoire Traitement du Signal et de l'Image, Institut National de la Santé et de la Recherche Médicale, Rennes, France
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40
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Cai L, Wang J, Guo Y, Lu M, Dong Y, Wei X. Altered inter-frequency dynamics of brain networks in disorder of consciousness. J Neural Eng 2020; 17:036006. [PMID: 32311694 DOI: 10.1088/1741-2552/ab8b2c] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Growing evidence have linked disorders of consciousness (DOC) with the changes in frequency-specific functional networks. However, the alteration of inter-frequency dynamics in brain networks remain largely unknown. In this study, we investigated the network integration and segregation across frequency bands in a multiplex network framework. APPROACH Resting-state EEG data were recorded and analysed from 19 patients in minimally conscious state, 35 patients in unresponsive wakefulness syndrome (UWS) and 23 healthy controls. Frequency-based multiplex (cross-frequency) networks were reconstructed by integrating the five frequency-specific networks. Multiplex graph metrics, named multiplex participation coefficient and multiplex clustering coefficient, were employed to assess the network topology of subjects with different levels of consciousness. MAIN RESULTS Results revealed DOC networks, compared to those of healthy controls, may work at a less optimal point (closer to complete disorder) with increased integration and decreased segregation considering inter-frequency dynamics. Both metrics show increased spatial and temporal variability with the consciousness levels. Moreover, significant correlation can be found between the alteration of cross-frequency networks in DOC patients and their behavioural performance at both local and global scales. SIGNIFICANCE These findings may contribute to the development of EEG network study and benefit our understanding of the processes of consciousness and their pathophysiology for DOC.
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Affiliation(s)
- Lihui Cai
- School of Electrical and Information Engineering, Tianjin University, Tianjin, People's Republic of China
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Kustermann T, Ata Nguepnjo Nguissi N, Pfeiffer C, Haenggi M, Kurmann R, Zubler F, Oddo M, Rossetti AO, De Lucia M. Brain functional connectivity during the first day of coma reflects long-term outcome. NEUROIMAGE-CLINICAL 2020; 27:102295. [PMID: 32563037 PMCID: PMC7305428 DOI: 10.1016/j.nicl.2020.102295] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 04/30/2020] [Accepted: 05/04/2020] [Indexed: 01/02/2023]
Abstract
Coma patients show different connectivity patterns depending on long-term outcome. Time-variance of functional connectivity is an early prognostic marker for coma patients. Connectivity patterns observed in chronic patients may develop early after coma onset.
Objective In patients with disorders of consciousness (DOC), properties of functional brain networks at rest are informative of the degree of consciousness impairment and of long-term outcome. Here we investigate whether connectivity differences between patients with favorable and unfavorable outcome are already present within 24 h of coma onset. Methods We prospectively recorded 63-channel electroencephalography (EEG) at rest during the first day of coma after cardiac arrest. We analyzed 98 adults, of whom 57 survived beyond unresponsive wakefulness. Functional connectivity was estimated by computing the ‘debiased weighted phase lag index’ over epochs of five seconds duration. We evaluated the network’s topological features, including clustering coefficient, path length, modularity and participation coefficient and computed their variance over time. Finally, we estimated the predictive value of these topological features for patients’ outcomes by splitting the patient sample in training and test datasets. Results Group-level analysis revealed lower clustering coefficient, higher modularity and path length variance in patients with favorable compared to those with unfavorable outcomes (p < 0.01). Within all features, the path length variance in the network provided the best positive predictive value (PPV) for favorable outcome and specificity for unfavorable outcome in the test dataset (PPV: 0.83, p < 0.01; specificity: 0.86, p < 0.01) with above-chance negative predictive value and accuracy. Of note, the exclusion of patients with epileptiform activity (20 in total) eliminates all false positive predictions (n = 6) for path length variance. Interpretation Topological features of functional connectivity differ as a function of long-term outcome in patients on the first day of coma. These differences are not interpretable in terms of consciousness levels as all patients were in a deep unconscious state. The time variance of path length is informative of comatose patients’ outcome, as patients with favorable outcome exhibit a richer repertoire of path length than those with unfavorable outcomes.
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Affiliation(s)
- Thomas Kustermann
- Laboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) & University of Lausanne, Switzerland.
| | | | | | - Matthias Haenggi
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Rebekka Kurmann
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Frédéric Zubler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Mauro Oddo
- Department of Intensive Care Medicine, University Hospital (CHUV) & University of Lausanne, Switzerland
| | - Andrea O Rossetti
- Department of Neurology, University Hospital (CHUV) & University of Lausanne, Switzerland
| | - Marzia De Lucia
- Laboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) & University of Lausanne, Switzerland
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Cai L, Wei X, Wang J, Yi G, Lu M, Dong Y. Characterization of network switching in disorder of consciousness at multiple time scales. J Neural Eng 2020; 17:026024. [PMID: 32097898 DOI: 10.1088/1741-2552/ab79f5] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Recent works have shown that flexible information processing is closely related to the reconfiguration of human brain networks underlying brain functions. However, the role of network switching for consciousness is poorly explored and whether such transition can indicate the behavioral performance of patients with disorders of consciousness (DOC) remains unknown. Here, we investigate the relationship between the switching of brain networks (states) over time and the consciousness levels. APPROACH By applying multilayer network methods, we calculated time-resolved functional connectivity from source-level EEG data in different frequency bands. At various time scales, we explored how the human brain changes its community structure and traverses across defined network states (integrated and segregated states) in subjects with different consciousness levels. MAIN RESULTS Network switching in the human brain is decreased with increasing time scale opposite to that in random systems. Transitions of community assignment (denoted by flexibility) are negatively correlated with the consciousness levels (particularly in the alpha band) at short time scales. At long time scales, the opposite trend is found. Compared to healthy controls, patients show a new balance between dynamic segregation and integration, with decreased proportion and mean duration of segregated state (contrary to those of integrated state) at small scales. SIGNIFICANCE These findings may contribute to the development of EEG-based network analysis and shed new light on the pathological mechanisms of neurological disorders like DOC.
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Affiliation(s)
- Lihui Cai
- School of Electrical and Information Engineering, Tianjin University, Tianjin, People's Republic of China
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Modolo J, Hassan M, Wendling F, Benquet P. Decoding the circuitry of consciousness: From local microcircuits to brain-scale networks. Netw Neurosci 2020; 4:315-337. [PMID: 32537530 PMCID: PMC7286300 DOI: 10.1162/netn_a_00119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 12/09/2019] [Indexed: 01/25/2023] Open
Abstract
Identifying the physiological processes underlying the emergence and maintenance of consciousness is one of the most fundamental problems of neuroscience, with implications ranging from fundamental neuroscience to the treatment of patients with disorders of consciousness (DOCs). One major challenge is to understand how cortical circuits at drastically different spatial scales, from local networks to brain-scale networks, operate in concert to enable consciousness, and how those processes are impaired in DOC patients. In this review, we attempt to relate available neurophysiological and clinical data with existing theoretical models of consciousness, while linking the micro- and macrocircuit levels. First, we address the relationships between awareness and wakefulness on the one hand, and cortico-cortical and thalamo-cortical connectivity on the other hand. Second, we discuss the role of three main types of GABAergic interneurons in specific circuits responsible for the dynamical reorganization of functional networks. Third, we explore advances in the functional role of nested oscillations for neural synchronization and communication, emphasizing the importance of the balance between local (high-frequency) and distant (low-frequency) activity for efficient information processing. The clinical implications of these theoretical considerations are presented. We propose that such cellular-scale mechanisms could extend current theories of consciousness.
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Affiliation(s)
- Julien Modolo
- University of Rennes, INSERM, LTSI-U1099, Rennes, France
| | - Mahmoud Hassan
- University of Rennes, INSERM, LTSI-U1099, Rennes, France
| | | | - Pascal Benquet
- University of Rennes, INSERM, LTSI-U1099, Rennes, France
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Thompson WH, Kastrati G, Finc K, Wright J, Shine JM, Poldrack RA. Time-varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation. Hum Brain Mapp 2020; 41:2347-2356. [PMID: 32058633 PMCID: PMC7268033 DOI: 10.1002/hbm.24950] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 01/10/2020] [Accepted: 02/03/2020] [Indexed: 11/07/2022] Open
Abstract
In network neuroscience, temporal network models have gained popularity. In these models, network properties have been related to cognition and behavior. Here, we demonstrate that calculating nodal properties that are dependent on temporal community structure (such as the participation coefficient [PC]) in time‐varying contexts can potentially lead to misleading results. Specifically, with regards to the participation coefficient, increases in integration can be inferred when the opposite is occurring. Further, we present a temporal extension to the PC measure (temporal PC) that circumnavigates this problem by jointly considering all community partitions assigned to a node through time. The proposed method allows us to track a node's integration through time while adjusting for the possible changes in the community structure of the overall network.
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Affiliation(s)
- William Hedley Thompson
- Department of Psychology, Stanford University, Stanford, California.,Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Granit Kastrati
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
| | - Karolina Finc
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University in Toruń, Toruń, Poland
| | - Jessey Wright
- Department of Psychology, Stanford University, Stanford, California.,Department of Philosophy, Stanford University, Stanford, California
| | - James M Shine
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
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