Disorders of ConsciousnessNCT05949528
Clinical Evaluation of Chronic Consciousness Disorders Using Resting-state EEG and ERP
An observational study of Disorders of Consciousness, run by First Affiliated Hospital of Zhejiang University at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 40 to 90 yearsHealthy volunteers can take part
- Conditions the study is for
- Disorders of Consciousness
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 12 Dec 2021to 30 Jun 2024
- How long will I take part?
- 6 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 90 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: 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.
Who may be able to join
- Patients diagnosed with COMA /VS/MCS
- The course of disease was more than 4 weeks
- The vital signs were stable and able to tolerate the test process
- Complete skull
- Right-handed, no history of ear disease or hearing loss before onset
Who cannot join
- History of epilepsy
- Sedatives
- Muscle relaxants and epileptic prophylaxis within 24 hours
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Yi Ling
Hangzhou, Zhejiang, 310000
Yi Ling, 15168236137, lywenzhoumc@163.com
About this study
Official title: Assessing Chronic Consciousness Disorders in Patients Using Clinical Evaluation With Resting-state EEG and ERPs: an Extensive Study Exploring Efficacy and Diagnostic Potential of EEG and ERP Measurements
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.
Read the detailed description
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.
- Conditions
- Disorders of Consciousness
- Being tested
- no intervention
- Keywords
- resting-state electroencephalograms, ERP, microstate
- Medical subject headings
- Consciousness Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
no interventionOther
no intervention
| Study group | Role | What they receive |
|---|---|---|
| Healthy controls (HCs) | Healthy controls (HCs) Receives: Other: no intervention | |
| Emerged from Minimally Conscious State (EMCS) | Emerged from Minimally Conscious State (EMCS): recovery of functional object uses or communication from chronic Receives: Other: no intervention | |
| Minimally conscious state (MCS) | Minimally conscious state (MCS): have reproducible signs of awareness and exhibit fluctuations in consciousness Receives: Other: no intervention | |
| Vegetative state (VS) | Vegetative state (VS): can open their eyes and preserve sleep-wake cycles, but unaware of themselves and their surroundings Receives: Other: no intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 6 months
Secondary outcomes (2)
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.
Measured: 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
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jul 2023
- Study start12 Dec 2021
- Primary completion30 Dec 2023 (estimated)
- Study completion30 Jun 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Disorders of ConsciousnessStudies of the same condition, open and under way.
- More trials run by First Affiliated Hospital of Zhejiang UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- First Affiliated Hospital of Zhejiang University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Benyuan Luo, Study chair, Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05949528
- Sponsor's protocol ID
- EEG assessment
- Status last verified
- Oct 2022
- First submitted
- 14 Mar 2022
- Last updated
- 18 Jul 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 41987185
Study at a glance
From the registry record, last updated by the sponsor on 18 Jul 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Assessing Chronic Consciousness Disorders in Patients Using Clinical Evaluation With Resting-state EEG and ERPs: an Extensive Study Exploring Efficacy and Diagnostic Potential of EEG and ERP Measurements
- ClinicalTrials.gov ID
- NCT05949528
- Acronym / sponsor ID
- EEG assessment
- Conditions
- Disorders of Consciousness
- Keywords
- resting-state electroencephalograms, ERP, microstate
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- First Affiliated Hospital of Zhejiang UniversityUniversity, hospital or other
- Study start
- 12 Dec 2021 (actual)
- Primary completion
- 30 Dec 2023 (estimated)
- Study completion
- 30 Jun 2024 (estimated)
- First posted
- 18 Jul 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 6 Months
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Healthy controls (HCs) | – | Other: no interventionHealthy controls (HCs) |
| Emerged from Minimally Conscious State (EMCS) | – | Other: no interventionEmerged from Minimally Conscious State (EMCS): recovery of functional object uses or communication from chronic |
| Minimally conscious state (MCS) | – | Other: no interventionMinimally conscious state (MCS): have reproducible signs of awareness and exhibit fluctuations in consciousness |
| Vegetative state (VS) | – | Other: no interventionVegetative state (VS): can open their eyes and preserve sleep-wake cycles, but unaware of themselves and their surroundings |
Who may qualify
- Ages
- 40 to 90 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (5 listed)
- Patients diagnosed with COMA /VS/MCS
- The course of disease was more than 4 weeks
- The vital signs were stable and able to tolerate the test process
- Complete skull
- Right-handed, no history of ear disease or hearing loss before onset
Key exclusion criteria (3 listed)
- History of epilepsy
- Sedatives
- Muscle relaxants and epileptic prophylaxis within 24 hours
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| no interventionno intervention | Other | Emerged from Minimally Conscious State (EMCS), Healthy controls (HCs), Minimally conscious state (MCS), Vegetative state (VS) |
Primary outcome
Spectrum analysis of chronic disorders of consciousness
Time frame: 6 months
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.
Duration of each microstate
Time frame: 6 months
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.
Occurrence of each microstate
Time frame: 6 months
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.
Global explained variance (GEV) of each microstate
Time frame: 6 months
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.
2 secondary outcomes
Coma Recovery Scale-Revised(CRS-R)
Time frame: 30 minutes before samples collection
Glasgow Outcome Scale (GOS)
Time frame: 6 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Jul 2023.
- Derived from this study: PubMed 41987185Mou 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Benyuan Luo study chair
Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Disorders of Consciousness, run by First Affiliated Hospital of Zhejiang University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 18 Jul 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yi Ling (15168236137, lywenzhoumc@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Spectrum analysis of chronic disorders of consciousness (time frame: 6 months). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05949528, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Jul 2023.