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Wang J, Lai Q, Han J, Qin P, Wu H. Neuroimaging biomarkers for the diagnosis and prognosis of patients with disorders of consciousness. Brain Res 2024; 1843:149133. [PMID: 39084451 DOI: 10.1016/j.brainres.2024.149133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 05/29/2024] [Accepted: 07/25/2024] [Indexed: 08/02/2024]
Abstract
The progress in neuroimaging and electrophysiological techniques has shown substantial promise in improving the clinical assessment of disorders of consciousness (DOC). Through the examination of both stimulus-induced and spontaneous brain activity, numerous comprehensive investigations have explored variations in brain activity patterns among patients with DOC, yielding valuable insights for clinical diagnosis and prognostic purposes. Nonetheless, reaching a consensus on precise neuroimaging biomarkers for patients with DOC remains a challenge. Therefore, in this review, we begin by summarizing the empirical evidence related to neuroimaging biomarkers for DOC using various paradigms, including active, passive, and resting-state approaches, by employing task-based fMRI, resting-state fMRI (rs-fMRI), electroencephalography (EEG), and positron emission tomography (PET) techniques. Subsequently, we conducted a review of studies examining the neural correlates of consciousness in patients with DOC, with the findings holding potential value for the clinical application of DOC. Notably, previous research indicates that neuroimaging techniques have the potential to unveil covert awareness that conventional behavioral assessments might overlook. Furthermore, when integrated with various task paradigms or analytical approaches, this combination has the potential to significantly enhance the accuracy of both diagnosis and prognosis in DOC patients. Nonetheless, the stability of these neural biomarkers still needs additional validation, and future directions may entail integrating diagnostic and prognostic methods with big data and deep learning approaches.
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Affiliation(s)
- Jiaying Wang
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Qiantu Lai
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China
| | - Junrong Han
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Pengmin Qin
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou 510631, China; Pazhou Lab, Guangzhou 510330, China.
| | - Hang Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China.
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Lo CCH, Woo PYM, Cheung VCK. Task-based EEG and fMRI paradigms in a multimodal clinical diagnostic framework for disorders of consciousness. Rev Neurosci 2024; 35:775-787. [PMID: 38804042 DOI: 10.1515/revneuro-2023-0159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 05/09/2024] [Indexed: 05/29/2024]
Abstract
Disorders of consciousness (DoC) are generally diagnosed by clinical assessment, which is a predominantly motor-driven process and accounts for up to 40 % of non-communication being misdiagnosed as unresponsive wakefulness syndrome (UWS) (previously known as prolonged/persistent vegetative state). Given the consequences of misdiagnosis, a more reliable and objective multimodal protocol to diagnosing DoC is needed, but has not been produced due to concerns regarding their interpretation and reliability. Of the techniques commonly used to detect consciousness in DoC, task-based paradigms (active paradigms) produce the most unequivocal result when findings are positive. It is well-established that command following (CF) reliably reflects preserved consciousness. Task-based electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can detect motor-independent CF and reveal preserved covert consciousness in up to 14 % of UWS patients. Accordingly, to improve the diagnostic accuracy of DoC, we propose a practical multimodal clinical decision framework centered on task-based EEG and fMRI, and complemented by measures like transcranial magnetic stimulation (TMS-EEG).
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Affiliation(s)
- Chris Chun Hei Lo
- School of Biomedical Sciences, and Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Peter Yat Ming Woo
- Division of Neurosurgery, Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Vincent C K Cheung
- School of Biomedical Sciences, and Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
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3
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Xu C, Yuan Z, Chen Z, Liao Z, Li S, Feng Y, Tang Z, Nian J, Huang X, Zhong H, Xie Q. Perturbational complexity index in assessing responsiveness to rTMS treatment in patients with disorders of consciousness: a cross-over randomized controlled trial study. J Neuroeng Rehabil 2024; 21:167. [PMID: 39300529 DOI: 10.1186/s12984-024-01455-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 09/02/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND Disorders of Consciousness (DoC) caused by severe brain injuries represent a challenging clinical entity, which is easy to misdiagnosis and lacks effective treatment options. Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive neuroelectric stimulation method that shows promise in improving consciousness for DoC, especially in minimally conscious state (MCS). However, there is little evidence of its effectiveness, especially in RCT studies. METHODS Twenty MCS patients participated in a double-blind, randomized, crossover, sham-controlled clinical study to evaluate the safety and efficacy of rTMS for MCS. Subjects were randomized into two groups: one group received rTMS-active for 10 consecutive days (n = 10), and the other group received rTMS-sham for 10 consecutive days (n = 10). After a 10-day washout period, the two groups were crossed over and received the opposite treatment. the rTMS protocol consisted of 2,000 pulses per day in the left dorsolateral prefrontal cortex (L-DLPFC), sent at 10 Hz. The stimulation intensity was 90% of the resting motor threshold. Coma Recovery Scale Revised (CRS-R), the main evaluation index, was evaluated before and after each phase in a double-blind manner. Meanwhile RS-EEG and TMS-EEG data were acquired and relative alpha power (RAP), and perturbational complexity index based on state transitions (PCIst) were caculated. RESULTS One-way ANOVA revealed significantly higher scores in rTMS-active treatment compared to rTMS-sham across various measures, including CRS-R total score, RAP, PCIst (all P < 0.05). Among the 20 MCS patients, 7 (35%) were identified as responders following rTMS treatment. Compared to rTMS-sham, responder scores for CRS-R, RAP, and PCIst (all P < 0.05) were significantly elevated after rTMS-active treatment. Conversely, there was no significant difference observed in non-responders. Furthermore, post-hoc analysis revealed that baseline PCIst was significantly higher in responders than non-responders. Upon a 6-month follow-up, CRS-R scores significantly increased in all 20 patients (P = 0.026). However, the responder group exhibited a more favorable prognosis compared to the non-responder group (P = 0.031). CONCLUSIONS Applying 10 Hz rTMS to L-DLPFC significantly increased consciousness level in MCS patients. PCIst is a neurophysiological index that has the potential to evaluate and predict therapeutic efficacy. TRIAL REGISTRATION www. CLINICALTRIALS gov , identifier: NCT05187000.
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Affiliation(s)
- Chengwei Xu
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, China
- School of Rehabilitation Sciences, Southern Medical University, 1023 Shatai SouthRoad, Guangzhou, Guangdong, 510515, China
| | - Zhanxing Yuan
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai SouthRoad, Guangzhou, Guangdong, 510515, China
| | - Zerong Chen
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Ziqin Liao
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Shuiyan Li
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Yanqi Feng
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Ziqiang Tang
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Jichan Nian
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Xiyan Huang
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Haili Zhong
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Qiuyou Xie
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, China.
- Department of hyperbaric oxygenation, Zhujiang Hospital of Southern Medical University, Guangzhou, China.
- School of Biomedical Engineering, Southern Medical University, 1023 Shatai SouthRoad, Guangzhou, Guangdong, 510515, China.
- School of Rehabilitation Sciences, Southern Medical University, 1023 Shatai SouthRoad, Guangzhou, Guangdong, 510515, China.
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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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Gallucci A, Varoli E, Del Mauro L, Hassan G, Rovida M, Comanducci A, Casarotto S, Lo Re V, Romero Lauro LJ. Multimodal approaches supporting the diagnosis, prognosis and investigation of neural correlates of disorders of consciousness: A systematic review. Eur J Neurosci 2024; 59:874-933. [PMID: 38140883 DOI: 10.1111/ejn.16149] [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: 12/12/2022] [Revised: 08/30/2023] [Accepted: 09/11/2023] [Indexed: 12/24/2023]
Abstract
The limits of the standard, behaviour-based clinical assessment of patients with disorders of consciousness (DoC) prompted the employment of functional neuroimaging, neurometabolic, neurophysiological and neurostimulation techniques, to detect brain-based covert markers of awareness. However, uni-modal approaches, consisting in employing just one of those techniques, are usually not sufficient to provide an exhaustive exploration of the neural underpinnings of residual awareness. This systematic review aimed at collecting the evidence from studies employing a multimodal approach, that is, combining more instruments to complement DoC diagnosis, prognosis and better investigating their neural correlates. Following the PRISMA guidelines, records from PubMed, EMBASE and Scopus were screened to select peer-review original articles in which a multi-modal approach was used for the assessment of adult patients with a diagnosis of DoC. Ninety-two observational studies and 32 case reports or case series met the inclusion criteria. Results highlighted a diagnostic and prognostic advantage of multi-modal approaches that involve electroencephalography-based (EEG-based) measurements together with neuroimaging or neurometabolic data or with neurostimulation. Multimodal assessment deepened the knowledge on the neural networks underlying consciousness, by showing correlations between the integrity of the default mode network and the different clinical diagnosis of DoC. However, except for studies using transcranial magnetic stimulation combined with electroencephalography, the integration of more than one technique in most of the cases occurs without an a priori-designed multi-modal diagnostic approach. Our review supports the feasibility and underlines the advantages of a multimodal approach for the diagnosis, prognosis and for the investigation of neural correlates of DoCs.
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Affiliation(s)
- Alessia Gallucci
- Ph.D. Program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- NeuroMi (Neuroscience Center), University of Milano-Bicocca, Milan, Italy
| | - Erica Varoli
- Neurology Service, Department of Diagnostic and Therapeutic Services, Istituto di Ricovero e Cura a Carattere Scientifico-Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione (IRCCS ISMETT), Palermo, Italy
| | - Lilia Del Mauro
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Gabriel Hassan
- Department of Biomedical and Clinical Sciences, University of Milan, Italy
| | - Margherita Rovida
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Angela Comanducci
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
- Università Campus Bio-Medico di Roma, Rome, Italy
| | - Silvia Casarotto
- Department of Biomedical and Clinical Sciences, University of Milan, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Vincenzina Lo Re
- Neurology Service, Department of Diagnostic and Therapeutic Services, Istituto di Ricovero e Cura a Carattere Scientifico-Istituto Mediterraneo per i Trapianti e Terapie ad Alta Specializzazione (IRCCS ISMETT), Palermo, Italy
| | - Leonor J Romero Lauro
- NeuroMi (Neuroscience Center), University of Milano-Bicocca, Milan, Italy
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
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Wang Y, Dang Y, Bai Y, Xia X, Li X. Evaluating the effect of spinal cord stimulation on patient with disorders of consciousness: A TMS-EEG study. Comput Biol Med 2023; 166:107547. [PMID: 37806053 DOI: 10.1016/j.compbiomed.2023.107547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/29/2023] [Accepted: 09/28/2023] [Indexed: 10/10/2023]
Abstract
OBJECTIVE The application of spinal cord stimulation (SCS) in the treatment of disorders of consciousness (DOC) has attracted attention, but its effect on brain activity is still unknown. Transcranial magnetic stimulation combined with EEG (TMS-EEG) can measure cortical activity, which can evaluate the effect of SCS on DOC. METHODS We record 20 DOC patients' CRS-R values and TMS-EEG data before and after one-session SCS (Pre-SCS and Post-SCS). 20 DOC patients including 10 patients with unresponsive wakefulness syndrome (UWS) and 10 patients with minimally conscious states (MCS). TMS evoked potential (TEP) was used to measure the changes of cortical activity in DOC patients between Pre-SCS and Post-SCS. Firstly, we used the global mean field potential (GMFP) and fast perturbational complexity index (PCIst) to compare the temporal changes of patients' cortical activity. Then, we obtained the frequency feature (natural frequency, NF) based on the TEP time-frequency analysis, and compared the changes of natural frequency between Pre-SCS and Post-SCS. Finally, the study explored the relationship between the patient's baseline CRS-R values and changes of TMS evoked cortical activity in time and frequency domains. RESULTS After SCS, MCS and UWS groups almost have no changes of CRS-R values (MCS: 9.9 ± 1.52 at Pre-SCS, 10.2 ± 1.48 at Post-SCS; UWS: 5.6 ± 1.26 at Pre-SCS, 5.7 ± 1.34 at Post-SCS). MCS group showed significant increases of GMFP amplitude (around 100 ms and 300 ms) and PCIst values at Post-SCS (p < 0.05). UWS group had no significant changes (p > 0.05). Besides, SCS induced the significant increases of natural frequency for MCS group(p < 0.05), but not for UWS group. At last, the study found that all patient's baseline CRS-R values were significantly correlated with ΔPCIst (r = 0.67, p < 0.005), and ΔNF (r = 0.72, p < 0.001). CONCLUSIONS SCS can modulate cortical activity of DOC patient, including temporal complexity and natural frequency. The changes of cortical activity caused by SCS are related to patients' consciousness level. TMS-EEG can evaluate the effect of SCS on DOC patients.
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Affiliation(s)
- Yong Wang
- Zhuhai UM Science & Technology Research Institute, Zhuhai, 519031, China
| | - Yuanyuan Dang
- Medical School of Chinese PLA, Department of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China; Department of Neurosurgery, the Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yang Bai
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China; Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang, 330006, China
| | - Xiaoyu Xia
- Medical School of Chinese PLA, Department of Neurosurgery, the First Medical Center of Chinese PLA General Hospital, Beijing, China; Department of Neurosurgery, the Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, 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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Vatrano M, Nemirovsky IE, Tonin P, Riganello F. Assessing Consciousness through Neurofeedback and Neuromodulation: Possibilities and Challenges. Life (Basel) 2023; 13:1675. [PMID: 37629532 PMCID: PMC10455583 DOI: 10.3390/life13081675] [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: 07/03/2023] [Revised: 07/27/2023] [Accepted: 07/30/2023] [Indexed: 08/27/2023] Open
Abstract
Neurofeedback is a non-invasive therapeutic approach that has gained traction in recent years, showing promising results for various neurological and psychiatric conditions. It involves real-time monitoring of brain activity, allowing individuals to gain control over their own brainwaves and improve cognitive performance or alleviate symptoms. The use of electroencephalography (EEG), such as brain-computer interface (BCI), transcranial direct current stimulation (tDCS), and transcranial magnetic stimulation (TMS), has been instrumental in developing neurofeedback techniques. However, the application of these tools in patients with disorders of consciousness (DoC) presents unique challenges. In this narrative review, we explore the use of neurofeedback in treating patients with DoC. More specifically, we discuss the advantages and challenges of using tools such as EEG neurofeedback, tDCS, TMS, and BCI for these conditions. Ultimately, we hope to provide the neuroscientific community with a comprehensive overview of neurofeedback and emphasize its potential therapeutic applications in severe cases of impaired consciousness levels.
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Affiliation(s)
- Martina Vatrano
- S. Anna Institute, Research in Advanced Neurorehabilitation, Via Siris, 11, 88900 Crotone, Italy;
| | - Idan Efim Nemirovsky
- Department of Physics and Astronomy, Brain and Mind Institute, University of Western Ontario, London, ON N6A 3K7, Canada;
| | - Paolo Tonin
- S. Anna Institute, Research in Advanced Neurorehabilitation, Via Siris, 11, 88900 Crotone, Italy;
| | - Francesco Riganello
- S. Anna Institute, Research in Advanced Neurorehabilitation, Via Siris, 11, 88900 Crotone, Italy;
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9
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Vucic S, Stanley Chen KH, Kiernan MC, Hallett M, Benninger DH, Di Lazzaro V, Rossini PM, Benussi A, Berardelli A, Currà A, Krieg SM, Lefaucheur JP, Long Lo Y, Macdonell RA, Massimini M, Rosanova M, Picht T, Stinear CM, Paulus W, Ugawa Y, Ziemann U, Chen R. Clinical diagnostic utility of transcranial magnetic stimulation in neurological disorders. Updated report of an IFCN committee. Clin Neurophysiol 2023; 150:131-175. [PMID: 37068329 PMCID: PMC10192339 DOI: 10.1016/j.clinph.2023.03.010] [Citation(s) in RCA: 55] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/28/2023] [Accepted: 03/09/2023] [Indexed: 03/31/2023]
Abstract
The review provides a comprehensive update (previous report: Chen R, Cros D, Curra A, Di Lazzaro V, Lefaucheur JP, Magistris MR, et al. The clinical diagnostic utility of transcranial magnetic stimulation: report of an IFCN committee. Clin Neurophysiol 2008;119(3):504-32) on clinical diagnostic utility of transcranial magnetic stimulation (TMS) in neurological diseases. Most TMS measures rely on stimulation of motor cortex and recording of motor evoked potentials. Paired-pulse TMS techniques, incorporating conventional amplitude-based and threshold tracking, have established clinical utility in neurodegenerative, movement, episodic (epilepsy, migraines), chronic pain and functional diseases. Cortical hyperexcitability has emerged as a diagnostic aid in amyotrophic lateral sclerosis. Single-pulse TMS measures are of utility in stroke, and myelopathy even in the absence of radiological changes. Short-latency afferent inhibition, related to central cholinergic transmission, is reduced in Alzheimer's disease. The triple stimulation technique (TST) may enhance diagnostic utility of conventional TMS measures to detect upper motor neuron involvement. The recording of motor evoked potentials can be used to perform functional mapping of the motor cortex or in preoperative assessment of eloquent brain regions before surgical resection of brain tumors. TMS exhibits utility in assessing lumbosacral/cervical nerve root function, especially in demyelinating neuropathies, and may be of utility in localizing the site of facial nerve palsies. TMS measures also have high sensitivity in detecting subclinical corticospinal lesions in multiple sclerosis. Abnormalities in central motor conduction time or TST correlate with motor impairment and disability in MS. Cerebellar stimulation may detect lesions in the cerebellum or cerebello-dentato-thalamo-motor cortical pathways. Combining TMS with electroencephalography, provides a novel method to measure parameters altered in neurological disorders, including cortical excitability, effective connectivity, and response complexity.
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Affiliation(s)
- Steve Vucic
- Brain, Nerve Research Center, The University of Sydney, Sydney, Australia.
| | - Kai-Hsiang Stanley Chen
- Department of Neurology, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
| | - Matthew C Kiernan
- Brain and Mind Centre, The University of Sydney; and Department of Neurology, Royal Prince Alfred Hospital, Australia
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, Maryland, United States
| | - David H Benninger
- Department of Neurology, University Hospital of Lausanne (CHUV), Switzerland
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, Rome, Italy
| | - Paolo M Rossini
- Department of Neurosci & Neurorehab IRCCS San Raffaele-Rome, Italy
| | - Alberto Benussi
- Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alfredo Berardelli
- IRCCS Neuromed, Pozzilli; Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Antonio Currà
- Department of Medico-Surgical Sciences and Biotechnologies, Alfredo Fiorini Hospital, Sapienza University of Rome, Terracina, LT, Italy
| | - Sandro M Krieg
- Department of Neurosurgery, Technical University Munich, School of Medicine, Klinikum rechts der Isar, Munich, Germany
| | - Jean-Pascal Lefaucheur
- Univ Paris Est Creteil, EA4391, ENT, Créteil, France; Clinical Neurophysiology Unit, Henri Mondor Hospital, AP-HP, Créteil, France
| | - Yew Long Lo
- Department of Neurology, National Neuroscience Institute, Singapore General Hospital, Singapore, and Duke-NUS Medical School, Singapore
| | | | - Marcello Massimini
- Dipartimento di Scienze Biomediche e Cliniche, Università degli Studi di Milano, Milan, Italy; Istituto Di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences University of Milan, Milan, Italy
| | - Thomas Picht
- Department of Neurosurgery, Charité-Universitätsmedizin Berlin, Cluster of Excellence: "Matters of Activity. Image Space Material," Humboldt University, Berlin Simulation and Training Center (BeST), Charité-Universitätsmedizin Berlin, Germany
| | - Cathy M Stinear
- Department of Medicine Waipapa Taumata Rau, University of Auckland, Auckland, Aotearoa, New Zealand
| | - Walter Paulus
- Department of Neurology, Ludwig-Maximilians-Universität München, München, Germany
| | - Yoshikazu Ugawa
- Department of Human Neurophysiology, School of Medicine, Fukushima Medical University, Japan
| | - Ulf Ziemann
- Department of Neurology and Stroke, Eberhard Karls University of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany; Hertie Institute for Clinical Brain Research, Eberhard Karls University of Tübingen, Otfried-Müller-Straße 27, 72076 Tübingen, Germany
| | - Robert Chen
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital-UHN, Division of Neurology-University of Toronto, Toronto Canada
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10
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Ding Z, Guan L, He W, Gu H, Wang Y, Li X. Spatial characteristics of closed-loop TMS-EEG with occipital alpha-phase synchronized. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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11
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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: 6] [Impact Index Per Article: 3.0] [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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