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Imaging the Neural Network Connectivity on Patients With Mild Cognitive Impairment

NCT01927653 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 108

Last updated 2018-07-18

No results posted yet for this study

Summary

The hypothesis tested if the diffusion properties in the base line, such as mean diffusivity or kurtosis, can differentiate two subtypes of MCI and predict the clinical outcome in Patients. The hypothesis further supports the correlation of the measured diffusion properties and the disease severity. We therefore proposed to investigate the potential value of diffusion properties as a possible tool to monitor the disease progression. The disease related changes in neural connectivity will be investigated.

1. The diffusion MRI could provide an improved diagnosis of Alzheimer's Disease and Mild cognitive Impairment.

Explanation:

The deposition of the macromolecules such as beta amyloid in the brain and the associated neuron death of the patient could lead to observable changes in tissue microenvironment. The related changes would lead to alterations in either the amplitude or distribution of water diffusion. In turn it could be detected in diffusion tensor and kurtosis.
2. aMCI is a preclinical state of AD and dMCI is from a different etiology, which can be differentially diagnosis by MRI. Diffusion Imaging could help to predict the clinical outcome Explanation

Conditions

Sponsors & Collaborators

  • Chang Gung Memorial Hospital

    lead OTHER

Principal Investigators

  • Jiun-Jie Wang, PhD · ChangGung University

Eligibility

Min Age
55 Years
Max Age
70 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2011-01-31
Primary Completion
2016-12-31
Completion
2017-02-28

Countries

  • Taiwan

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT01927653 on ClinicalTrials.gov