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Hansen MJ. Modelling developments in consciousness within a multidimensional framework. Neurosci Conscious 2024; 2024:niae026. [PMID: 38895541 PMCID: PMC11184344 DOI: 10.1093/nc/niae026] [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: 08/10/2023] [Revised: 01/17/2024] [Accepted: 06/03/2024] [Indexed: 06/21/2024] Open
Abstract
A recent advancement in consciousness science has been the introduction of a multidimensional framework of consciousness. This framework has been applied to global states of consciousness, including psychedelic states and disorders of consciousness, and the consciousness of non-human animals. The multidimensional framework enables a finer parsing of both various states of consciousness and forms of animal consciousness, paving the way for new scientific investigations into consciousness. In this paper, the multidimensional model is expanded by constructing temporal profiles. This expansion allows for the modelling of changes in consciousness across the life cycles of organisms and the progression over time of disorders of consciousness. The result of this expansion is 2-fold: (i) it enables new modes of comparison, both across stages of development and across species; (ii) it proposes that more attention be given to the various types of fluctuations that occur in patients who are suffering from disorders of consciousness.
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Affiliation(s)
- Mads Jørgensen Hansen
- Department of Philosophy and History of Ideas, School of Culture and Society, Aarhus University, Aarhus 8000, Denmark
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2
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Apablaza-Yevenes DE, Corsi-Cabrera M, Martinez-Guerrero A, Northoff G, Romaniello C, Farinelli M, Bertoletti E, Müller MF, Muñoz-Torres Z. Stationary stable cross-correlation pattern and task specific deviations in unresponsive wakefulness syndrome as well as clinically healthy subjects. PLoS One 2024; 19:e0300075. [PMID: 38489260 PMCID: PMC10942032 DOI: 10.1371/journal.pone.0300075] [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: 06/01/2023] [Accepted: 02/20/2024] [Indexed: 03/17/2024] Open
Abstract
Brain dynamics is highly non-stationary, permanently subject to ever-changing external conditions and continuously monitoring and adjusting internal control mechanisms. Finding stationary structures in this system, as has been done recently, is therefore of great importance for understanding fundamental dynamic trade relationships. Here we analyse electroencephalographic recordings (EEG) of 13 subjects with unresponsive wakefulness syndrome (UWS) during rest and while being influenced by different acoustic stimuli. We compare the results with a control group under the same experimental conditions and with clinically healthy subjects during overnight sleep. The main objective of this study is to investigate whether a stationary correlation pattern is also present in the UWS group, and if so, to what extent this structure resembles the one found in healthy subjects. Furthermore, we extract transient dynamical features via specific deviations from the stationary interrelation pattern. We find that (i) the UWS group is more heterogeneous than the two groups of healthy subjects, (ii) also the EEGs of the UWS group contain a stationary cross-correlation pattern, although it is less pronounced and shows less similarity to that found for healthy subjects and (iii) deviations from the stationary pattern are notably larger for the UWS than for the two groups of healthy subjects. The results suggest that the nervous system of subjects with UWS receive external stimuli but show an overreaching reaction to them, which may disturb opportune information processing.
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Affiliation(s)
- David E. Apablaza-Yevenes
- Instituto de Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Morelos, México
| | - María Corsi-Cabrera
- Unidad de Investigación en Neurodesarrollo, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México
| | | | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, Ontario, Canada
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, People’s Republic of China
- Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China
| | | | | | | | - Markus F. Müller
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad de México, México
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Morelos, México
- Centro Internacional de Ciencias A.C., Morelos, México
| | - Zeidy Muñoz-Torres
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad de México, México
- Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, México
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Ma X, Qi Y, Xu C, Weng Y, Yu J, Sun X, Yu Y, Wu Y, Gao J, Li J, Shu Y, Duan S, Luo B, Pan G. How well do neural signatures of resting-state EEG detect consciousness? A large-scale clinical study. Hum Brain Mapp 2024; 45:e26586. [PMID: 38433651 PMCID: PMC10910334 DOI: 10.1002/hbm.26586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 12/12/2023] [Accepted: 12/21/2023] [Indexed: 03/05/2024] Open
Abstract
The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large-scale clinical resting-state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG-based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph-based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index-related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.
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Affiliation(s)
- Xiulin Ma
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, and the Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Yu Qi
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, and the Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Chuan Xu
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Sir Run Run Shaw Hospital, Hangzhou, China
| | - Yijie Weng
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Jie Yu
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xuyun Sun
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Yamei Yu
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- Sir Run Run Shaw Hospital, Hangzhou, China
| | - Yuehao Wu
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jian Gao
- Department of Rehabilitation, Hangzhou Mingzhou Brain Rehabilitation Hospital, Hangzhou, China
| | - Jingqi Li
- Department of Rehabilitation, Hangzhou Mingzhou Brain Rehabilitation Hospital, Hangzhou, China
| | - Yousheng Shu
- Department of Neurosurgery, Jinshan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institute for Translational Brain Research, Fudan University, Shanghai, China
| | - Shumin Duan
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, and the Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
| | - Benyan Luo
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, and the Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Gang Pan
- Department of Neurobiology and Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, and the Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University, Hangzhou, China
- The State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
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Wu M, Concolato M, Sorger B, Yu Y, Li X, Luo B, Riecke L. Acoustic-electric trigeminal-nerve stimulation enhances functional connectivity in patients with disorders of consciousness. CNS Neurosci Ther 2024; 30:e14385. [PMID: 37525451 PMCID: PMC10928333 DOI: 10.1111/cns.14385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 06/29/2023] [Accepted: 07/16/2023] [Indexed: 08/02/2023] Open
Abstract
AIM Disruption of functional brain connectivity is thought to underlie disorders of consciousness (DOC) and recovery of impaired connectivity is suggested as an indicator of consciousness restoration. We recently found that rhythmic acoustic-electric trigeminal-nerve stimulation (i.e., musical stimulation synchronized to electrical stimulation of the trigeminal nerve) in the gamma band can improve consciousness in patients with DOC. Here, we investigated whether these beneficial stimulation effects are mediated by alterations in functional connectivity. METHODS Sixty-three patients with DOC underwent 5 days of gamma, beta, or sham acoustic-electric trigeminal-nerve stimulation. Resting-state electroencephalography was measured before and after the stimulation and functional connectivity was assessed using phase-lag index (PLI). RESULTS We found that gamma stimulation induces an increase in gamma-band PLI. Further characterization revealed that the enhancing effect is (i) specific to the gamma band (as we observed no comparable change in beta-band PLI and no effect of beta-band acoustic-electric stimulation or sham stimulation), (ii) widely spread across the cortex, and (iii) accompanied by improvements in patients' auditory abilities. CONCLUSION These findings show that gamma acoustic-electric trigeminal-nerve stimulation can improve resting-state functional connectivity in the gamma band, which in turn may be linked to auditory abilities and/or consciousness restoration in DOC patients.
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Affiliation(s)
- Min Wu
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Marta Concolato
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
- Department of Developmental Psychology and SocializationUniversity of PadovaPadovaItaly
| | - Bettina Sorger
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Yamei Yu
- Department of Neurology and Brain Medical Centre, First Affiliated Hospital, School of MedicineZhejiang UniversityHangzhouChina
| | - Xiaoxia Li
- Department of Neurology and Brain Medical Centre, First Affiliated Hospital, School of MedicineZhejiang UniversityHangzhouChina
| | - Benyan Luo
- Department of Neurology and Brain Medical Centre, First Affiliated Hospital, School of MedicineZhejiang UniversityHangzhouChina
| | - Lars Riecke
- Department of Cognitive Neuroscience, Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
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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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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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Wang J, Gao X, Xiang Z, Sun F, Yang Y. Evaluation of consciousness rehabilitation via neuroimaging methods. Front Hum Neurosci 2023; 17:1233499. [PMID: 37780959 PMCID: PMC10537959 DOI: 10.3389/fnhum.2023.1233499] [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: 06/02/2023] [Accepted: 08/30/2023] [Indexed: 10/03/2023] Open
Abstract
Accurate evaluation of patients with disorders of consciousness (DoC) is crucial for personalized treatment. However, misdiagnosis remains a serious issue. Neuroimaging methods could observe the conscious activity in patients who have no evidence of consciousness in behavior, and provide objective and quantitative indexes to assist doctors in their diagnosis. In the review, we discussed the current research based on the evaluation of consciousness rehabilitation after DoC using EEG, fMRI, PET, and fNIRS, as well as the advantages and limitations of each method. Nowadays single-modal neuroimaging can no longer meet the researchers` demand. Considering both spatial and temporal resolution, recent studies have attempted to focus on the multi-modal method which can enhance the capability of neuroimaging methods in the evaluation of DoC. As neuroimaging devices become wireless, integrated, and portable, multi-modal neuroimaging methods will drive new advancements in brain science research.
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Affiliation(s)
| | | | | | - Fangfang Sun
- College of Automation, Hangzhou Dianzi University, Hangzhou, 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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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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11
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Wei X, Yan Z, Cai L, Lu M, Yi G, Wang J, Dong Y. Aberrant temporal correlations of ongoing oscillations in disorders of consciousness on multiple time scales. Cogn Neurodyn 2023; 17:633-645. [PMID: 37265651 PMCID: PMC10229524 DOI: 10.1007/s11571-022-09852-9] [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: 05/17/2021] [Revised: 05/19/2022] [Accepted: 07/06/2022] [Indexed: 11/27/2022] Open
Abstract
Changes in neural oscillation amplitude across states of consciousness has been widely reported, but little is known about the link between temporal dynamics of these oscillations on different time scales and consciousness levels. To address this question, we analyzed amplitude fluctuation of the oscillations extracted from spontaneous resting-state EEG recorded from the patients with disorders of consciousness (DOC) and healthy controls. Detrended fluctuation analysis (DFA) and measures of life-time and waiting-time were employed to characterize the temporal structure of EEG oscillations on long time scales (1-20 s) and short time scales (< 1 s), in groups with different consciousness states: patients in minimally conscious state (MCS), patients with unresponsive wakefulness syndrome (UWS) and healthy subjects. Results revealed increased DFA exponents that implies higher long-range temporal correlations (LRTC), especially in the central brain area in alpha and beta bands. On short time scales, declined bursts of oscillations were also observed. All the metrics exhibited lower individual variability in the UWS or MCS group, which may be attributed to the reduced spatial variability of oscillation dynamics. In addition, the temporal dynamics of EEG oscillations showed significant correlations with the behavioral responsiveness of patients. In summary, our findings shows that loss of consciousness is accompanied by alternation of temporal structure in neural oscillations on multiple time scales, and thus may help uncover the mechanism of underlying neuronal correlates of consciousness. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09852-9.
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Affiliation(s)
- Xile Wei
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Zhuang Yan
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Lihui Cai
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Meili Lu
- School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, 300222 China
| | - Guosheng Yi
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Yueqing Dong
- Xincheng Hospital of Tianjin University, Tianjin, China
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12
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Di Gregorio F, La Porta F, Petrone V, Battaglia S, Orlandi S, Ippolito G, Romei V, Piperno R, Lullini G. Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach. Biomedicines 2022; 10:biomedicines10081897. [PMID: 36009445 PMCID: PMC9405912 DOI: 10.3390/biomedicines10081897] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/04/2022] [Accepted: 07/29/2022] [Indexed: 11/18/2022] Open
Abstract
Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients’ etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.
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Affiliation(s)
- Francesco Di Gregorio
- UO Medicina Riabilitativa e Neuroriabilitazione, Azienda Unità Sanitaria Locale, 40133 Bologna, Italy
| | - Fabio La Porta
- IRCCS Istituto delle Scienze Neurologiche di Bologna
- Correspondence:
| | | | - Simone Battaglia
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
- Dipartimento di Psicologia, Università di Torino, 10124 Torino, Italy
| | - Silvia Orlandi
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Viale Risorgimento, 2, 40136 Bologna, Italy
| | - Giuseppe Ippolito
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | - Vincenzo Romei
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | | | - Giada Lullini
- IRCCS Istituto delle Scienze Neurologiche di Bologna
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13
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Arnts H, Tewarie P, van Erp WS, Overbeek BU, Stam CJ, Lavrijsen JCM, Booij J, Vandertop WP, Schuurman R, Hillebrand A, van den Munckhof P. Clinical and neurophysiological effects of central thalamic deep brain stimulation in the minimally conscious state after severe brain injury. Sci Rep 2022; 12:12932. [PMID: 35902627 PMCID: PMC9334292 DOI: 10.1038/s41598-022-16470-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/11/2022] [Indexed: 12/01/2022] Open
Abstract
Deep brain stimulation (DBS) of the central thalamus is an experimental treatment for restoration of impaired consciousness in patients with severe acquired brain injury. Previous results of experimental DBS are heterogeneous, but significant improvements in consciousness have been reported. However, the mechanism of action of DBS remains unknown. We used magnetoencephalography to study the direct effects of DBS of the central thalamus on oscillatory activity and functional connectivity throughout the brain in a patient with a prolonged minimally conscious state. Different DBS settings were used to improve consciousness, including two different stimulation frequencies (50 Hz and 130 Hz) with different effective volumes of tissue activation within the central thalamus. While both types of DBS resulted in a direct increase in arousal, we found that DBS with a lower frequency (50 Hz) and larger volume of tissue activation was associated with a stronger increase in functional connectivity and neural variability throughout the brain. Moreover, this form of DBS was associated with improvements in visual pursuit, a reduction in spasticity, and improvement of swallowing, eight years after loss of consciousness. However, after DBS, all neurophysiological markers remained significantly lower than in healthy controls and objective increases in consciousness remained limited. Our findings provide new insights on the mechanistic understanding of neuromodulatory effects of DBS of the central thalamus in humans and suggest that DBS can re-activate dormant functional brain networks, but that the severely injured stimulated brain still lacks the ability to serve cognitive demands.
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Affiliation(s)
- Hisse Arnts
- Department of Neurosurgery, Amsterdam Neurosciences, Systems & Network Neurosciences, Amsterdam UMC (Location AMC), University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
| | - Prejaas Tewarie
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Willemijn S van Erp
- Department of Primary and Community Care, Center for Family Medicine, Geriatric Care and Public Health, Radboud University Medical Center, Nijmegen, The Netherlands.,Accolade Zorg, Bosch en Duin, The Netherlands.,Libra Rehabilitation & Audiology, Tilburg, The Netherlands
| | - Berno U Overbeek
- Department of Primary and Community Care, Center for Family Medicine, Geriatric Care and Public Health, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jan C M Lavrijsen
- Department of Primary and Community Care, Center for Family Medicine, Geriatric Care and Public Health, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jan Booij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - William P Vandertop
- Department of Neurosurgery, Amsterdam Neurosciences, Systems & Network Neurosciences, Amsterdam UMC (Location AMC), University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Rick Schuurman
- Department of Neurosurgery, Amsterdam Neurosciences, Systems & Network Neurosciences, Amsterdam UMC (Location AMC), University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Pepijn van den Munckhof
- Department of Neurosurgery, Amsterdam Neurosciences, Systems & Network Neurosciences, Amsterdam UMC (Location AMC), University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
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14
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Zhang Y, Wang K, Yu W, Guo X, Wen J, Luo Y. Minimal EEG channel selection for depression detection with connectivity features during sleep. Comput Biol Med 2022; 147:105690. [DOI: 10.1016/j.compbiomed.2022.105690] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/29/2022] [Accepted: 05/31/2022] [Indexed: 11/30/2022]
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15
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Zhang L, Zhang R, Guo Y, Zhao D, Li S, Chen M, Shi L, Yao D, Gao J, Wang X, Hu Y. Assessing residual motor function in patients with disorders of consciousness by brain network properties of task-state EEG. Cogn Neurodyn 2022; 16:609-620. [PMID: 35603051 PMCID: PMC9120323 DOI: 10.1007/s11571-021-09741-7] [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: 08/06/2021] [Revised: 09/27/2021] [Accepted: 10/24/2021] [Indexed: 10/19/2022] Open
Abstract
Recent achievements in evaluating the residual consciousness of patients with disorders of consciousness (DOCs) have demonstrated that spontaneous or evoked electroencephalography (EEG) could be used to improve consciousness state diagnostic classification. Recent studies showed that the EEG signal of the task-state could better characterize the conscious state and cognitive ability of the brain, but it has rarely been used in consciousness assessment. A cue-guide motor task experiment was designed, and task-state EEG were collected from 18 patients with unresponsive wakefulness syndrome (UWS), 29 patients in a minimally conscious state (MCS), and 19 healthy controls. To obtain the markers of residual motor function in patients with DOC, the event-related potential (ERP), scalp topography, and time-frequency maps were analyzed. Then the coherence (COH) and debiased weighted phase lag index (dwPLI) networks in the delta, theta, alpha, beta, and gamma bands were constructed, and the correlations of network properties and JFK Coma Recovery Scale-Revised (CRS-R) motor function scores were calculated. The results showed that there was an obvious readiness potential (RP) at the Cz position during the motor preparation process in the MCS group, but no RP was observed in the UWS group. Moreover, the node degree properties of the COH network in the theta and alpha bands and the global efficiency properties of the dwPLI network in the theta band were significantly greater in the MCS group compared to the UWS group. The above network properties and CRS-R motor function scores showed a strong linear correlation. These findings demonstrated that the brain network properties of task-state EEG could be markers of residual motor function of DOC patients. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-021-09741-7.
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Affiliation(s)
- Lipeng Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
| | - Rui Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
- Institute of Neuroscience of Zhengzhou University, Zhengzhou, China
| | - Yongkun Guo
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Dexiao Zhao
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shizheng Li
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
| | - Mingming Chen
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
- Institute of Neuroscience of Zhengzhou University, Zhengzhou, China
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, China
- Beijing National Research Center for Information Science and Technology, Beijing, China
| | - Dezhong Yao
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 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
- Institute of Neuroscience of Zhengzhou University, Zhengzhou, China
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16
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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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17
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de Zambotti M, Yuksel D, Kiss O, Barresi G, Arra N, Volpe L, King C, Baker FC. A virtual reality-based mind–body approach to downregulate psychophysiological arousal in adolescent insomnia. Digit Health 2022; 8:20552076221107887. [PMID: 35733879 PMCID: PMC9208061 DOI: 10.1177/20552076221107887] [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: 05/07/2022] [Accepted: 05/30/2022] [Indexed: 11/15/2022] Open
Abstract
Objective In this study, we describe the rationale, supported by preliminary data, for a novel, digital, immersive virtual reality (VR)-based mind–body approach, designed to reduce bedtime arousal in adolescents with insomnia. Methods Fifty-two high-school students (16–20 years; 32 female) with ( N = 18) and without ( N = 34) DSM-5 insomnia symptoms engaged with 20 min of immersive VR-guided meditation and paced breathing (0.1 Hz) ( intervention condition) and 20 min of quiet activity ( control condition), right before bedtime, on two separate evenings. Results The intervention resulted in acute autonomic and cortical modulation ( p < 0.05), leading to reduced physiological arousal (↓heart rate, ↓cortisol) compared with the control condition, with similar effects in adolescents with and without insomnia. No significant changes were detected for cognitive arousal levels. During the intervention, all participants were able to achieve the targeted 0.1 Hz breathing rate, and the majority experienced no discomfort associated with the VR exposure. However, 30–40% of the participants experienced some trouble slowing down their breathing. Conclusions The study provides supporting preliminary evidence for the mechanism behind a novel VR-based digital approach, designed to regulate psychophysiological arousal levels by acting on neurocognitive and autonomic pathways. Further studies (e.g. randomized clinical trials) are needed to evaluate the isolated and synergistic effects of its components (e.g. VR vs. VR + paced breathing), and its efficacy, acceptance, and feasibility in alleviating insomnia symptoms in adolescents.
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Affiliation(s)
| | - Dilara Yuksel
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Orsolya Kiss
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Giacinto Barresi
- Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Nicole Arra
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Laila Volpe
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Christopher King
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Pediatric Pain Research Center (PPRC), Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Fiona C Baker
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
- Brain Function Research Group, School of Physiology, University of the Witwatersrand, Johannesburg, South Africa
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18
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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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19
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Salimi M, Javadi AH, Nazari M, Bamdad S, Tabasi F, Parsazadegan T, Ayene F, Karimian M, Gholami-Mahtaj L, Shadnia S, Jamaati H, Salimi A, Raoufy MR. Nasal Air Puff Promotes Default Mode Network Activity in Mechanically Ventilated Comatose Patients: A Noninvasive Brain Stimulation Approach. Neuromodulation 2021; 25:1351-1363. [PMID: 35088756 DOI: 10.1016/j.neurom.2021.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 10/01/2021] [Accepted: 10/26/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Coma state and loss of consciousness are associated with impaired brain activity, particularly gamma oscillations, that integrate functional connectivity in neural networks, including the default mode network (DMN). Mechanical ventilation (MV) in comatose patients can aggravate brain activity, which has decreased in coma, presumably because of diminished nasal airflow. Nasal airflow, known to drive functional neural oscillations, synchronizing distant brain networks activity, is eliminated by tracheal intubation and MV. Hence, we proposed that rhythmic nasal air puffing in mechanically ventilated comatose patients may promote brain activity and improve network connectivity. MATERIALS AND METHODS We recorded electroencephalography (EEG) from 15 comatose patients (seven women) admitted to the intensive care unit because of opium poisoning and assessed the activity, complexity, and connectivity of the DMN before and during the nasal air-puff stimulation. Nasal cavity air puffing was done through a nasal cannula controlled by an electrical valve (open duration of 630 ms) with a frequency of 0.2 Hz (ie, 12 puff/min). RESULTS Our analyses demonstrated that nasal air puffing enhanced the power of gamma oscillations (30-100 Hz) in the DMN. In addition, we found that the coherence and synchrony between DMN regions were increased during nasal air puffing. Recurrence quantification and fractal dimension analyses revealed that EEG global complexity and irregularity, typically seen in wakefulness and conscious state, increased during rhythmic nasal air puffing. CONCLUSIONS Rhythmic nasal air puffing, as a noninvasive brain stimulation method, opens a new window to modifying the brain connectivity integration in comatose patients. This approach may potentially influence comatose patients' outcomes by increasing brain reactivity and network connectivity.
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Affiliation(s)
- Morteza Salimi
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Amir-Homayoun Javadi
- School of Psychology, University of Kent, Canterbury, UK; School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
| | - Milad Nazari
- Electrical Engineering Department, Sharif University of Technology, Tehran, Iran; Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark; The Danish Research Institute of Translational Neuroscience (DANDRITE), Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Sobhan Bamdad
- Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
| | - Farhad Tabasi
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran; Institute for Brain Sciences and Cognition, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Tannaz Parsazadegan
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Fahime Ayene
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Maede Karimian
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Leila Gholami-Mahtaj
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Shahin Shadnia
- Department of Clinical Toxicology, Excellence Center of Clinical Toxicology, Loghman Hakim Hospital Poison Center, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamidreza Jamaati
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Salimi
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran; Institute for Brain Sciences and Cognition, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
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20
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Lei L, Liu K, Yang Y, Doubliez A, Hu X, Xu Y, Zhou Y. Spatio-temporal analysis of EEG features during consciousness recovery in patients with disorders of consciousness. Clin Neurophysiol 2021; 133:135-144. [PMID: 34864400 DOI: 10.1016/j.clinph.2021.08.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 08/10/2021] [Accepted: 08/29/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVE As consciousness recovery is not only dynamic but also involves interactions between various brain regions, elucidating the mechanism of recovery requires tracking cortical activity in spatio-temporal dimensions. METHODS We tracked the cortical activities of 40 patients (mean age: 54.38 years; 28 males; 21 patients with minimally conscious states) with disorders of consciousness, and collected a total of 156 electroencephalographic signals. We investigated the longitudinal changes in EEG nonlinear dynamic features (i.e., approximate entropy, sample entropy, and Lempel-Ziv complexity) and relative wavelet energy along with consciousness recovery. RESULTS Global EEG features showed a non-monotonic trend during consciousness recovery (P < 0.05). When the level of consciousness of patients was transferred to a minimally conscious state from an unresponsive wakefulness syndrome/ vegetative state, an inflection point appeared in the EEG features. The EEG feature change trends between the injured and uninjured areas were dissimilar (P < 0.05). Importantly, the degree of dissimilarity increased non-monotonically across the levels of consciousness (P < 0.05). CONCLUSIONS EEG recovery was non-monotonic and dissimilar in spatio-temporal dimensions, with an inflection point. SIGNIFICANCE These findings further clarify the process of consciousness recovery and provide assistance in exploring the mechanism of consciousness recovery.
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Affiliation(s)
- Ling Lei
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
| | - Kehong Liu
- Wu Jing Hospital, Rehabilitation Center, Hangzhou, Zhejiang 310051, China
| | - Yong Yang
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
| | - Alice Doubliez
- Paris Descartes University, 45 rue des Saints-Peres, Paris 75006, France
| | - Xiaohua Hu
- Wu Jing Hospital, Rehabilitation Center, Hangzhou, Zhejiang 310051, China
| | - Ying Xu
- College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
| | - Yixing Zhou
- First People's Hospital of Zhaoqing City, No. 9 Donggang East Road, Duanzhou District, Zhaoqing 526060, China.
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21
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Spontaneous transient brain states in EEG source space in disorders of consciousness. Neuroimage 2021; 240:118407. [PMID: 34280527 DOI: 10.1016/j.neuroimage.2021.118407] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 06/28/2021] [Accepted: 07/15/2021] [Indexed: 02/06/2023] Open
Abstract
Spontaneous transient states were recently identified by functional magnetic resonance imaging and magnetoencephalography in healthy subjects. They organize and coordinate neural activity in brain networks. How spontaneous transient states are altered in abnormal brain conditions is unknown. Here, we conducted a transient state analysis on resting-state electroencephalography (EEG) source space and developed a state transfer analysis to patients with disorders of consciousness (DOC). They uncovered different neural coordination patterns, including spatial power patterns, temporal dynamics, spectral shifts, and connectivity construction varies at potentially very fast (millisecond) time scales, in groups with different consciousness levels: healthy subjects, patients in minimally conscious state (MCS), and patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Machine learning based on transient state features reveal high classification accuracy between MCS and VS/UWS. This study developed methodology of transient states analysis on EEG source space and abnormal brain conditions. Findings correlate spontaneous transient states with human consciousness and suggest potential roles of transient states in brain disease assessment.
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22
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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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Jain R, Ramakrishnan AG. Electrophysiological and Neuroimaging Studies - During Resting State and Sensory Stimulation in Disorders of Consciousness: A Review. Front Neurosci 2020; 14:555093. [PMID: 33041757 PMCID: PMC7522478 DOI: 10.3389/fnins.2020.555093] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 08/25/2020] [Indexed: 12/17/2022] Open
Abstract
A severe brain injury may lead to a disorder of consciousness (DOC) such as coma, vegetative state (VS), minimally conscious state (MCS) or locked-in syndrome (LIS). Till date, the diagnosis of DOC relies only on clinical evaluation or subjective scoring systems such as Glasgow coma scale, which fails to detect subtle changes and thereby results in diagnostic errors. The high rate of misdiagnosis and inability to predict the recovery of consciousness for DOC patients have created a huge research interest in the assessment of consciousness. Researchers have explored the use of various stimulation and neuroimaging techniques to improve the diagnosis. In this article, we present the important findings of resting-state as well as sensory stimulation methods and highlight the stimuli proven to be successful in the assessment of consciousness. Primarily, we review the literature based on (a) application/non-use of stimuli (i.e., sensory stimulation/resting state-based), (b) type of stimulation used (i.e., auditory, visual, tactile, olfactory, or mental-imagery), (c) electrophysiological signal used (EEG/ERP, fMRI, PET, EMG, SCL, or ECG). Among the sensory stimulation methods, auditory stimulation has been extensively used, since it is easier to conduct for these patients. Olfactory and tactile stimulation have been less explored and need further research. Emotionally charged stimuli such as subject’s own name or narratives in a familiar voice or subject’s own face/family pictures or music result in stronger responses than neutral stimuli. Studies based on resting state analysis have employed measures like complexity, power spectral features, entropy and functional connectivity patterns to distinguish between the VS and MCS patients. Resting-state EEG and fMRI are the state-of-the-art techniques and have a huge potential in predicting the recovery of coma patients. Further, EMG and mental-imagery based studies attempt to obtain volitional responses from the VS patients and thus could detect their command-following capability. This may provide an effective means to communicate with these patients. Recent studies have employed fMRI and PET to understand the brain-activation patterns corresponding to the mental imagery. This review promotes our knowledge about the techniques used for the diagnosis of patients with DOC and attempts to provide ideas for future research.
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Affiliation(s)
- Ritika Jain
- Medical Intelligence and Language Engineering Laboratory, Department of Electrical Engineering, Indian Institute of Science, Bengaluru, India
| | - Angarai Ganesan Ramakrishnan
- Medical Intelligence and Language Engineering Laboratory, Department of Electrical Engineering, Indian Institute of Science, Bengaluru, India
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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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Virtual Reality Based Cognitive Rehabilitation in Minimally Conscious State: A Case Report with EEG Findings and Systematic Literature Review. Brain Sci 2020; 10:brainsci10070414. [PMID: 32630179 PMCID: PMC7407378 DOI: 10.3390/brainsci10070414] [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: 04/14/2020] [Revised: 06/22/2020] [Accepted: 06/23/2020] [Indexed: 12/14/2022] Open
Abstract
Chronic disorders of consciousness cause a total or partial and fluctuating unawareness of the surrounding environment. Virtual reality (VR) can be useful as a diagnostic and/or a neurorehabilitation tool, and its effects can be monitored by means of both clinical and electroencephalography (EEG) data recording of brain activity. We reported on the case of a 17-year-old patient with a disorder of consciousness (DoC) who was provided with VR training to improve her cognitive-behavioral outcomes, which were assessed using clinical scales (the Coma Recovery Scale-Revised, the Disability Rating Scale, and the Rancho Los Amigos Levels of Cognitive Functioning), as well as EEG recording, during VR training sessions. At the end of the training, significant improvements in both clinical and neurophysiological outcomes were achieved. Then, we carried out a systematic review of the literature to investigate the role of EEG and VR in the management of patients with DoC. A search on PubMed, Web of Science, Scopus, and Google Scholar databases was performed, using the keywords: “disorders of consciousness” and “virtual reality”, or “EEG”. The results of the literature review suggest that neurophysiological data in combination with VR could be useful in evaluating the reactions induced by different paradigms in DoC patients, helping in the differential diagnosis. In conclusion, the EEG plus VR approach used with our patient could be promising to define the most appropriate stimulation protocol, so as to promote a better personalization of the rehabilitation program. However, further clinical trials, as well as meta-analysis of the literature, are needed to be affirmative on the role of VR in patients with DoC.
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26
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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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27
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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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28
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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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29
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Billeri L, Filoni S, Russo EF, Portaro S, Militi D, Calabrò RS, Naro A. Toward Improving Diagnostic Strategies in Chronic Disorders of Consciousness: An Overview on the (Re-)Emergent Role of Neurophysiology. Brain Sci 2020; 10:brainsci10010042. [PMID: 31936844 PMCID: PMC7016627 DOI: 10.3390/brainsci10010042] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 01/03/2020] [Accepted: 01/08/2020] [Indexed: 12/13/2022] Open
Abstract
The differential diagnosis of patients with Disorder of Consciousness (DoC), in particular in the chronic phase, is significantly difficult. Actually, about 40% of patients with unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS) are misdiagnosed. Indeed, only advanced paraclinical approaches, including advanced EEG analyses, can allow achieving a more reliable diagnosis, that is, discovering residual traces of awareness in patients with UWS (namely, functional Locked-In Syndrome (fLIS)). These approaches aim at capturing the residual brain network models, at rest or that may be activated in response to relevant stimuli, which may be appropriate for awareness to emerge (despite their insufficiency to generate purposeful motor behaviors). For this, different brain network models have been studied in patients with DoC by using sensory stimuli (i.e., passive tasks), probing response to commands (i.e., active tasks), and during resting-state. Since it can be difficult for patients with DoC to perform even simple active tasks, this scoping review aims at summarizing the current, innovative neurophysiological examination methods in resting state/passive modality to differentiate and prognosticate patients with DoC. We conclude that the electrophysiologically-based diagnostic procedures represent an important resource for diagnosis, prognosis, and, therefore, management of patients with DoC, using advance passive and resting state paradigm analyses for the patients who lie in the “greyzones” between MCS, UWS, and fLIS.
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Affiliation(s)
- Luana Billeri
- IRCCS Centro Neurolesi Bonino Pulejo, 98124 Messina, Italy; (L.B.); (S.P.); (A.N.)
| | - Serena Filoni
- Padre Pio Foundation and Rehabilitation Centers, San Giovanni Rotondo, 71013 Foggia, Italy;
- Correspondence: (S.F.); (R.S.C.); Tel.: +39-090-6012-8166 (R.S.C.)
| | | | - Simona Portaro
- IRCCS Centro Neurolesi Bonino Pulejo, 98124 Messina, Italy; (L.B.); (S.P.); (A.N.)
| | | | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi Bonino Pulejo, 98124 Messina, Italy; (L.B.); (S.P.); (A.N.)
- Correspondence: (S.F.); (R.S.C.); Tel.: +39-090-6012-8166 (R.S.C.)
| | - Antonino Naro
- IRCCS Centro Neurolesi Bonino Pulejo, 98124 Messina, Italy; (L.B.); (S.P.); (A.N.)
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Corchs S, Chioma G, Dondi R, Gasparini F, Manzoni S, Markowska-Kacznar U, Mauri G, Zoppis I, Morreale A. Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness. Front Neurosci 2019; 13:807. [PMID: 31447631 PMCID: PMC6691089 DOI: 10.3389/fnins.2019.00807] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Accepted: 07/19/2019] [Indexed: 12/16/2022] Open
Abstract
Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application.
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Affiliation(s)
- Silvia Corchs
- Department of Computer Science, University Milano-Bicocca, Milan, Italy
| | - Giovanni Chioma
- Behavioral Neurology, Montecatone Rehabilitation Institute, Imola, Italy
| | - Riccardo Dondi
- Department of Letter and Communication, University of Bergamo, Bergamo, Italy
| | | | - Sara Manzoni
- Department of Computer Science, University Milano-Bicocca, Milan, Italy
| | - Urszula Markowska-Kacznar
- Department of Computational Intelligence, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wroclaw, Poland
| | - Giancarlo Mauri
- Department of Computer Science, University Milano-Bicocca, Milan, Italy
| | - Italo Zoppis
- Department of Computer Science, University Milano-Bicocca, Milan, Italy
| | - Angela Morreale
- Behavioral Neurology, Montecatone Rehabilitation Institute, Imola, Italy
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31
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Prefrontal neural dynamics in consciousness. Neuropsychologia 2019; 131:25-41. [DOI: 10.1016/j.neuropsychologia.2019.05.018] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 05/17/2019] [Accepted: 05/20/2019] [Indexed: 12/11/2022]
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32
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Functional Brain Network Topology Discriminates between Patients with Minimally Conscious State and Unresponsive Wakefulness Syndrome. J Clin Med 2019; 8:jcm8030306. [PMID: 30841486 PMCID: PMC6463121 DOI: 10.3390/jcm8030306] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 02/23/2019] [Accepted: 02/27/2019] [Indexed: 12/11/2022] Open
Abstract
Consciousness arises from the functional interaction of multiple brain structures and their ability to integrate different complex patterns of internal communication. Although several studies demonstrated that the fronto-parietal and functional default mode networks play a key role in conscious processes, it is still not clear which topological network measures (that quantifies different features of whole-brain functional network organization) are altered in patients with disorders of consciousness. Herein, we investigate the functional connectivity of unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) patients from a topological network perspective, by using resting-state EEG recording. Network-based statistical analysis reveals a subnetwork of decreased functional connectivity in UWS compared to in the MCS patients, mainly involving the interhemispheric fronto-parietal connectivity patterns. Network topological analysis reveals increased values of local-community-paradigm correlation, as well as higher clustering coefficient and local efficiency in UWS patients compared to in MCS patients. At the nodal level, the UWS patients showed altered functional topology in several limbic and temporo-parieto-occipital regions. Taken together, our results highlight (i) the involvement of the interhemispheric fronto-parietal functional connectivity in the pathophysiology of consciousness disorders and (ii) an aberrant connectome organization both at the network topology level and at the nodal level in UWS patients compared to in the MCS patients.
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