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Qu S, Wu X, Tang Y, Zhang Q, Huang L, Cui B, Jiao S, Sun Q, Zeng F. Analyzing brain-activation responses to auditory stimuli improves the diagnosis of a disorder of consciousness by non-linear dynamic analysis of the EEG. Sci Rep 2024; 14:17446. [PMID: 39075138 PMCID: PMC11286939 DOI: 10.1038/s41598-024-67825-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 07/16/2024] [Indexed: 07/31/2024] Open
Abstract
Although auditory stimuli benefit patients with disorders of consciousness (DOC), the optimal stimulus remains unclear. We explored the most effective electroencephalography (EEG)-tracking method for eliciting brain responses to auditory stimuli and assessed its potential as a neural marker to improve DOC diagnosis. We collected 58 EEG recordings from patients with DOC to evaluate the classification model's performance and optimal auditory stimulus. Using non-linear dynamic analysis (approximate entropy [ApEn]), we assessed EEG responses to various auditory stimuli (resting state, preferred music, subject's own name [SON], and familiar music) in 40 patients. The diagnostic performance of the optimal stimulus-induced EEG classification for vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) was compared with the Coma Recovery Scale-Revision in 18 patients using the machine learning cascade forward backpropagation neural network model. Regardless of patient status, preferred music significantly activated the cerebral cortex. Patients in MCS showed increased activity in the prefrontal pole and central, occipital, and temporal cortices, whereas those in VS/UWS showed activity in the prefrontal and anterior temporal lobes. Patients in VS/UWS exhibited the lowest preferred music-induced ApEn differences in the central, middle, and posterior temporal lobes compared with those in MCS. The resting state ApEn value of the prefrontal pole (0.77) distinguished VS/UWS from MCS with 61.11% accuracy. The cascade forward backpropagation neural network tested for ApEn values in the resting state and preferred music-induced ApEn differences achieved an average of 83.33% accuracy in distinguishing VS/UWS from MCS (based on K-fold cross-validation). EEG non-linear analysis quantifies cortical responses in patients with DOC, with preferred music inducing more intense EEG responses than SON and familiar music. Machine learning algorithms combined with auditory stimuli showed strong potential for improving DOC diagnosis. Future studies should explore the optimal multimodal sensory stimuli tailored for individual patients.Trial registration: The study is registered in the Chinese Registry of Clinical Trials (Approval no: KYLL-2023-414, Registration code: ChiCTR2300079310).
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Affiliation(s)
- Sheng Qu
- Department of Rehabilitation, Second Hospital of Shandong University, No. 247, Beiyuan Avenue, Jinan, 250033, Shandong, China
| | - Xinchun Wu
- Department of Rehabilitation, Second Hospital of Shandong University, No. 247, Beiyuan Avenue, Jinan, 250033, Shandong, China
| | - Yaxiu Tang
- Department of Rehabilitation, Second Hospital of Shandong University, No. 247, Beiyuan Avenue, Jinan, 250033, Shandong, China
| | - Qi Zhang
- Department of Rehabilitation, Second Hospital of Shandong University, No. 247, Beiyuan Avenue, Jinan, 250033, Shandong, China
| | - Laigang Huang
- Department of Rehabilitation, Second Hospital of Shandong University, No. 247, Beiyuan Avenue, Jinan, 250033, Shandong, China
| | - Baojuan Cui
- Department of Rehabilitation, Second Hospital of Shandong University, No. 247, Beiyuan Avenue, Jinan, 250033, Shandong, China
| | - Shengxiu Jiao
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324, Jingwuwei 7Th Road, Jinan, 250000, Shandong, China
| | - Qiangsan Sun
- Department of Rehabilitation, Second Hospital of Shandong University, No. 247, Beiyuan Avenue, Jinan, 250033, Shandong, China
| | - Fanshuo Zeng
- Department of Rehabilitation, Second Hospital of Shandong University, No. 247, Beiyuan Avenue, Jinan, 250033, Shandong, China.
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Yin J, Xu G, Xie H, Liu Y, Dou Z, Shao B, Li Z. Effects of different frequencies music on cortical responses and functional connectivity in patients with minimal conscious state. JOURNAL OF BIOPHOTONICS 2024; 17:e202300427. [PMID: 38303080 DOI: 10.1002/jbio.202300427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/04/2023] [Accepted: 12/19/2023] [Indexed: 02/03/2024]
Abstract
The objective of this study was to investigate brain activation and functional network patterns during musical interventions in different frequency bands using functional near-infrared spectroscopy, and to provide a basis for more effective music therapy strategy selection for patients in minimally conscious state (MCS). Twenty six MCS patients and 20 healthy people were given music intervention with low frequency (31-180 Hz), medium frequency (180-4k Hz), and high frequency (4k-22k Hz) audio. In MCS patients, low frequency music intervention induced activation of left prefrontal cortex and left primary sensory cortex (S1), also a left-hemisphere lateralization effect of dorsolateral prefrontal cortex (DLPFC). And the functional connectivity of right DLPFC-right S1 was significantly improved by high frequency music intervention. The low frequency and high frequency music may contribute more than medium frequency music to the recovery of consciousness. This study also validated the effectiveness of fNIRS in studies of brain function in MCS patients.
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Affiliation(s)
- Jiahui Yin
- School of Physical Education, Shanghai University of Sport, Shanghai, China
| | - Gongcheng Xu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Hui Xie
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Ying Liu
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
- Key Laboratory of Neuro-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing, China
| | - Zulin Dou
- Department of Rehabilitation Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bin Shao
- School of Physical Education, Shanghai University of Sport, Shanghai, China
| | - Zengyong Li
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing, China
- Key Laboratory of Neuro-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Beijing, China
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Li J, Huang B, Wang F, Xie Q, Xu C, Huang H, Pan J. A Potential Prognosis Indicator Based on P300 Brain-Computer Interface for Patients with Disorder of Consciousness. Brain Sci 2022; 12:1556. [PMID: 36421880 PMCID: PMC9688541 DOI: 10.3390/brainsci12111556] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/30/2022] [Accepted: 11/11/2022] [Indexed: 08/08/2023] Open
Abstract
For patients with disorders of consciousness, such as unresponsive wakefulness syndrome (UWS) patients and minimally conscious state (MCS) patients, their long treatment cycle and high cost commonly put a heavy burden on the patient's family and society. Therefore, it is vital to accurately diagnose and predict consciousness recovery for such patients. In this paper, we explored the role of the P300 signal based on an audiovisual BCI in the classification and prognosis prediction of patients with disorders of consciousness. This experiment included 18 patients: 10 UWS patients and 8 MCS- patients. At the three-month follow-up, we defined patients with an improved prognosis (from UWS to MCS-, from UWS to MCS+, or from MCS- to MCS+) as "improved patients" and those who stayed in UWS/MCS as "not improved patients". First, we compared and analyzed different types of patients, and the results showed that the P300 detection accuracy rate of "improved" patients was significantly higher than that of "not improved" patients. Furthermore, the P300 detection accuracy of traumatic brain injury (TBI) patients was significantly higher than that of non-traumatic brain injury (NTBI, including acquired brain injury and cerebrovascular disease) patients. We also found that there was a positive linear correlation between P300 detection accuracy and CRS-R score, and patients with higher P300 detection accuracy were likely to achieve higher CRS-R scores. In addition, we found that the patients with higher P300 detection accuracies tend to have better prognosis in this audiovisual BCI. These findings indicate that the detection accuracy of P300 is significantly correlated with the level of consciousness, etiology, and prognosis of patients. P300 can be used to represent the preservation level of consciousness in clinical neurophysiology and predict the possibility of recovery in patients with disorders of consciousness.
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Affiliation(s)
- Jingcong Li
- School of Software, South China Normal University, Guangzhou 510631, China
- Pazhou Lab, Guangzhou 510631, China
| | - Biao Huang
- School of Software, South China Normal University, Guangzhou 510631, China
| | - Fei Wang
- School of Software, South China Normal University, Guangzhou 510631, China
- Pazhou Lab, Guangzhou 510631, China
| | - Qiuyou Xie
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou 510631, China
| | - Chengwei Xu
- Joint Research Centre for Disorders of Consciousness, Department of Rehabilitation Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou 510631, China
| | - Haiyun Huang
- School of Software, South China Normal University, Guangzhou 510631, China
- Pazhou Lab, Guangzhou 510631, China
| | - Jiahui Pan
- School of Software, South China Normal University, Guangzhou 510631, China
- Pazhou Lab, Guangzhou 510631, China
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Liu B, Zhang X, Li Y, Duan G, Hou J, Zhao J, Guo T, Wu D. tDCS-EEG for Predicting Outcome in Patients With Unresponsive Wakefulness Syndrome. Front Neurosci 2022; 16:771393. [PMID: 35812233 PMCID: PMC9263392 DOI: 10.3389/fnins.2022.771393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives We aimed to assess the role of transcranial direct current stimulation (tDCS) combined with electroencephalogram (EEG) for predicting prognosis in UWS cases. Methods This was a historical control study that enrolled 85 patients with UWS. The subjects were assigned to the control (without tDCS) and tDCS groups. Conventional treatments were implemented in both the control and tDCS groups, along with 40 multi-target tDCS sessions only in the tDCS group. Coma Recovery Scale-Revised (CRS-R) was applied at admission. The non-linear EEG index was evaluated after treatment. The modified Glasgow Outcome Scale (mGOS) was applied 12 months after disease onset. Results The mGOS improvement rate in the tDCS group (37.1%) was higher than the control value (22.0%). Linear regression analysis revealed that the local and remote cortical networks under unaffected pain stimulation conditions and the remote cortical network under affected pain stimulation conditions were the main relevant factors for mGOS improvement. Furthermore, the difference in prefrontal-parietal cortical network was used to examine the sensitivity of prognostic assessment in UWS patients. The results showed that prognostic sensitivity could be increased from 54.5% (control group) to 84.6% (tDCS group). Conclusions This study proposes a tDCS-EEG protocol for predicting the prognosis of UWS. With multi-target tDCS combined with EEG, the sensitivity of prognostic assessment in patients with UWS was improved. The recovery might be related to improved prefrontal-parietal cortical networks of the unaffected hemisphere.
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Affiliation(s)
- Baohu Liu
- Department of Rehabilitation, Wangjing Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Xu Zhang
- Department of Rehabilitation, Wangjing Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Yuanyuan Li
- Department of Rehabilitation, Wangjing Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Guoping Duan
- Department of Rehabilitation, Wangjing Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Jun Hou
- Department of Rehabilitation, Wangjing Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Jiayi Zhao
- Department of Rehabilitation, Wangjing Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Tongtong Guo
- Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Dongyu Wu
- Department of Rehabilitation, Wangjing Hospital of China Academy of Chinese Medical Sciences, Beijing, China
- *Correspondence: Dongyu Wu
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Entropy Metrics Correlating with Higher Residual Functioning in Patients with Chronic Disorders of Consciousness. Brain Sci 2022; 12:brainsci12030332. [PMID: 35326288 PMCID: PMC8946802 DOI: 10.3390/brainsci12030332] [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: 12/23/2021] [Revised: 02/14/2022] [Accepted: 02/26/2022] [Indexed: 11/24/2022] Open
Abstract
To test the ability of different entropy measures to classify patients with different conditions of chronic disorder of consciousness, we applied the Lempel–Ziv complexity, the amplitude coalition entropy (ACE), and the synchrony coalition entropy (SCE) to the EEG signals recorded in 32 patients, clinically evaluated using the coma recovery scale revised (CRS-R). All the entropy measures indicated that differences found in the theta and alpha bands can distinguish patients in a minimal consciousness state (MCS) with respect to those in a vegetative state/unresponsive wakefulness state (VS/UWS). These differences were significant comparing the entropy measure performed on the anterior region of the left hemisphere and midline region. The values of theta-alpha entropy positively correlated with those of the CRS-R scores. Among the entropy measures, ACE most often highlighted significant differences. The higher values found in MCS were for the less impaired patients, according to their CRS-R, suggest that the preservation of signal entropy on the anterior region of the dominant hemisphere correlates with better preservation of consciousness, even in chronic conditions.
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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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