Study on Cognitive Impairment of Insomnia Based on MRI

Insomnia Clinical Trial

Official title:

Research on Early Warning Technology to Explore Insomnia-related Cognitive Impairment Based on MRI Neurovascular Uncoupling

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05659511
• Other study ID #: 202211-16
• Status: Recruiting
• Start date: July 1, 2022
• Est. completion date: June 30, 2024

Study information

• Verified date: January 2023
• Source: Tang-Du Hospital
• Contact: Lin-Feng Yan
• Phone: 029-18691899018
• Email: ylf8342[@]163.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Insomnia is a common sleep disorder. In recent years, the incidence of insomnia is increasing worldwide. Studies point out that insomnia plays an important role in the pathogenesis of cognitive impairment. Although sleep and cognitive scales are the main methods to detect sleep quality and cognitive changes, there are problems such as strong subjectivity and poor repetition. There is an urgent need to use non-invasive and objective detection methods to assess the potential mechanisms of cognitive impairment caused by sleep disorders. Previous studies have shown that different brain states may show different neurovascular coupling (NVC) characteristics. However, after prolonged sleep deprivation, the evoked hemodynamics response was attenuated despite an increased electroencephalogram (EEG) signal response, suggesting that sustained neural activity may reduce vascular compliance. It is suggested that sleep disorder may lead to NVC disorder. However, whether sleep disorders regulate the mechanism of cognitive impairment in the brain through NVC disorders has not been demonstrated in vivo. Currently, functional magnetic resonance imaging (fMRI) can be used to study brain function and blood flow changes non-invasively. In our previous research, we combined cerebral blood flow (CBF) with mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo) and degree-centrality (DC), the early warning effect of fMRI features based on neurovascular uncoupling on early cognitive impairment was confirmed, providing a basis for further selection of functional imaging indicators. In conclusion, the present study proposes the scientific hypothesis that neurovascular decoupling-based MRI features are more appropriate for exploring the neural mechanisms underlying sleep disorders-induced brain cognitive impairment. The aim of this study is to establish an early warning and monitoring system for early non-invasive diagnosis and intervention of sleep-related cognitive impairment.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 684
• Est. completion date: June 30, 2024
• Est. primary completion date: June 30, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Sleep score meets the group standard
• Education time more than 8 years
• Without dementia
• Inform Consent Form

Exclusion Criteria:

• Pregnant woman
• Suffer from serious brain disease
• Magnetic resonance contraindications
• Image quality is too poor to deal with
• Lack of compliance

Related Conditions & MeSH terms

• Cognition Disorders
• Cognitive Disorder
• Cognitive Dysfunction
• Insomnia
• MRI
• Sleep Initiation and Maintenance Disorders

Intervention

Diagnostic Test:
• MRI
MRI data was acquired with a GE discovery MR750 3.0 T scanner using an eight-channel phased- array head coil. Foam padding was used to restrict head movement and ear plugs were used to eliminate scanner noise. During the acquisition period, all participants were asked to keep their eyes closed and not to think anything.

Locations

Country	Name	City	State
China	Tangdu Hospital	Xi'an	Shaanxi

Sponsors (1)

Lead Sponsor	Collaborator
Tang-Du Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Screening out early warning indicators of MCI in patients with insomnia	Based on the neurovascular uncoupled MRI features and imaging omics features of ID patients with MCI, the early warning indicators of MCI in ID patients were screened by machine learning algorithm.	baseline
Secondary	Construct an automatic and individualized accurate diagnosis model for insomnia with MCI	The structural MRI and functional MRI were used to analyze the biological changes or other mechanisms related to sleep disorders, and the clinical information and neuroimaging characteristics were combined to initially build an automatic and individualized accurate diagnostic model for insomnia and MCI with sensitivity, specificity and accuracy>80%.	through study completion, an average of 2 year