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Currently, there are significant challenges in the clinical assessment of patients with consciousness disorders, such as distinguishing between vegetative state (VS) and minimally conscious state (MCS), and predicting patient prognosis. This study aims to utilize different research techniques, such as auditory stimulation, as well as modified microstate methods, to enhance the disease classification and prognosis prediction of patients with chronic consciousness disorders.
The investigators collected resting-state electroencephalograms (EEGs) and EEGs under various event-related potential (ERP) stimuli from patients with chronic consciousness disorders, and performed analyses on these data. The resting-state EEGs were subjected to spectral analysis and microstate analysis. The ERP EEGs were analyzed in the time domain, as well as for phase coupling and other measures.Using these computed indicators, the investigators use machine learning, deep learning, and other methods to predict disease classification and prognosis assessment in patients with chronic consciousness disorders.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Healthy controls (HCs) | Healthy controls (HCs) |
| |
| Emerged from Minimally Conscious State (EMCS) | Emerged from Minimally Conscious State (EMCS): recovery of functional object uses or communication from chronic |
| |
| Minimally conscious state (MCS) | Minimally conscious state (MCS): have reproducible signs of awareness and exhibit fluctuations in consciousness |
| |
| Vegetative state (VS) | Vegetative state (VS): can open their eyes and preserve sleep-wake cycles, but unaware of themselves and their surroundings |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| no intervention | Other | no intervention |
|
| Measure | Description | Time Frame |
|---|---|---|
| Spectrum analysis of chronic disorders of consciousness | The EEG of 59 patients with disturbance of consciousness will be collected in resting state and listening to music, and the absolute power spectral density values (alpha,beta,theta,delta bands dB/Hz) will be calculated using spectral analysis. | 6 months |
| Duration of each microstate | The investigators conducted resting state EEG recordings on 59 patients with consciousness disorders and 32 healthy controls. The investigators refined the microstate method to accurately estimate topographical differences. The calculations were performed for measures of duration (ms). The duration of each microstate were utilized to predict disease classification and prognosis evaluation for patients with disturbance of consciousness. | 6 months |
| Occurrence of each microstate | The investigators conducted resting state EEG recordings on 59 patients with consciousness disorders and 32 healthy controls. The investigators refined the microstate analysis. The calculations were performed for measures of occurrence (times per minute). The occurrence of microstates were utilized to predict disease classification and prognosis evaluation for patients with disturbance of consciousness. | 6 months |
| Global explained variance (GEV) of each microstate | The investigators conducted resting state EEG recordings on 59 patients with consciousness disorders and 32 healthy controls. The investigators refined the microstate analysis. The calculations were performed for measures of GEV (%). The GEV of microstates were utilized to predict disease classification and prognosis evaluation for patients with disturbance of consciousness. | 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Coma Recovery Scale-Revised(CRS-R) | The Coma Recovery Scale-Revised (CRS-R) score was utilized to measure the severity of the condition. It comprises 23 items arranged hierarchically into six subscales, including auditory, visual, motor, oromotor/verbal, communication, and arousal processes. Reflexive activity is represented by the lowest item on each subscale, while cognitively mediated behaviors are portrayed by the highest items. The scale ranges from 0 (indicating the lowest level of consciousness) to 23 (indicating the highest level of consciousness). Generally, a higher score suggests a better level of consciousness, while a lower score suggests a lower level of consciousness. |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with chronic disorder of consciousness were enrolled in the Hangzhou Mingzhou Brain Rehabilitation Hospital, and healthy controls were all recorded by the First Affiliated Hospital of Zhejiang University.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yi Ling | Contact | 15168236137 | lywenzhoumc@163.com | |
| Fangping He | Contact | 13819114225 | lywenzhoumc@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Benyuan Luo | Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Yi Ling | Recruiting | Hangzhou | Zhejiang | 310000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41987185 | Derived | Mou C, Zhao J, Yan Z, Zhang L, Tian X, Wang Y, Wang H, Hu J, He Z, Ling Y, Kang A, Luo Q, Gao J, Ye X, Hong L, Li J, Yan T, Yu J, Luo B. An explainable multimodal machine learning model for diagnosing disorders of consciousness: evidence from a large multicenter Chinese cohort. J Transl Med. 2026 Apr 15;24(1):708. doi: 10.1186/s12967-026-08108-y. |
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| ID | Term |
|---|---|
| D003244 | Consciousness Disorders |
| ID | Term |
|---|---|
| D019954 | Neurobehavioral Manifestations |
| D009461 | Neurologic Manifestations |
| D009422 | Nervous System Diseases |
| D012816 | Signs and Symptoms |
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Electroencephalogram (EEG) refers to the resting state and event-related potential (ERP) of the brain.
| 30 minutes before samples collection |
| Glasgow Outcome Scale (GOS) | A GOS score ≥ 4 points is considered to indicate a good prognosis, while a GOS score < 4 points is considered to indicate a poor prognosis | 6 months |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D019965 | Neurocognitive Disorders |
| D001523 | Mental Disorders |