Imaging the Neural Network Connectivity on Patients With Mild Cognitive Impairment
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|ClinicalTrials.gov Identifier: NCT01927653|
Recruitment Status : Completed
First Posted : August 22, 2013
Last Update Posted : July 21, 2017
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.
The diffusion MRI could provide an improved diagnosis of Alzheimer's Disease and Mild cognitive Impairment.
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.
- 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
|Condition or disease|
|Mild Cognitive Impairment|
Mild Cognitive Impairment (MCI) referred to a decline of cognition in elder adults that are not of sufficient magnitude to meet the criteria for dementia. It is usually regarded as a transition state between patients of Alzheimer's Disease (AD) and the age matched healthy adults. It is a heterogeneous syndrome which can be divided into two subtypes: amnestic and dysexecutive. This 3 year proposal continues from a NSc funded project, in which we reported that the amnestic MCI could involve global white matter changes and therefore could be a preclinical status to AD. In contrast, no compromise in white matter status was found in patients of dysexecutive MCI. Therefore we proposed to further investigate the phenomena in this project.
The subjects will be divided into 3 groups: 30 patients with amnestic MCI, 30 with dysexecutive MCI and 30 healthy age-matched normal controls. Comprehensive neuropsychological examinations will be performed after detailed clinical history and physical screening, including Mini-Mental Status Examination, Clinical Dementia Rating and the Cognitive Abilities Screening Instrument. Successful candidate will be examined by 3T MRI, including diffusion imaging and high resolution T1 weighted anatomical images.
The current project proposed to examine the sensitivity and specificity of diffusion Magnetic Resonance Imaging, in the diagnosis of MCI and differential diagnosis of two subtypes. Both the conventional tensor derived indices and diffusion kurtosis will be compared. This is due to the fact that in a recently publication in Radiology, we reported an improved diagnostic performance on neurodegenerative disease from diffusion kurtosis than diffusion tensor. Secondly we will examine the regional changes of diffusion properties and correlated with the white matter involvement in patients. High resolution track density images will be implemented and compared with the susceptibility weighted imaging in an effort to address the underlying changes in pathophysiology. In the third year, the prognostic value of diffusion MRI will be determined. The optimal cutoff value of diffusion MRI in the prediction of conversion to Alzheimer's disease will be reported. The diffusion properties in patients with early conversion (the 2nd year) and late conversion (the 3rd year) will be compared.
It is expected that changes in diffusion can be used as an image based surrogate marker during the neurodegenerative process. The new insight into the temporal evolution of the diffusion MRI might help to understand the underlying etiology and pathophysiology between the amnestic and dysexecutive MCI patients, which can contribute to an early intervention strategy and might ultimately lead to an effective treatment.
|Study Type :||Observational|
|Actual Enrollment :||108 participants|
|Official Title:||Imaging the Neural Network Connectivity on Patients With Mild Cognitive Impairment|
|Study Start Date :||January 2011|
|Actual Primary Completion Date :||December 2016|
|Actual Study Completion Date :||February 2017|
The patients with amnestic MCI
The patients with single domain amnestic MCI have a Clinical Dementia Rating score of 0.5 with isolated memory impairment without deficits in other cognitive domains. A cutoff scores below 1.5 Standard Deviation (SD) (or 7 percentile) of one of the tests in domains of cognitions employed psychometric tests; They should meet the following criteria:
The patients with dysexecutive MCI
The patients of dMCI have relatively focal dysfunction in executive domain with the tests of memory, language and visuospatial skills within normal limits. The patients with single domain dysexecutive MCI should meet the following criteria:
The healthy volunteers
The healthy volunteers should be normal neuropsychological assessments as well as CDR=0 without significant neuropsychiatric disorder, right-handed, gender balanced and meet the following criteria:
- differentiation of MCI using diffusion MRI [ Time Frame: end of the first year ]
The following will be measured in order to assess the diagnostic performance of diffusion MRI
- the sensitivity and specificity determined by Receiver operatic characteristic analysis
- the correlation between the neuropsychiatry batter and the measured diffusivity To determine the diagnostic sensitivity and specificity of MCI using diffusion MRI.
With these two measurement, the outcome is to differentiate the subtypes of MCI
- Prognosis value of diffusion MRI [ Time Frame: end of the third year ]The clinical deterioration will be determined by the difference in neuropsychiatry battery between the third year and the first year. The analysis will be made with and without the first year diffusion MRI. It will then be determined if the first year diffusion MRI can predict the decline in the third year. An optimal cutoff value will be calculated.
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Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT01927653
|ChangGung Memorial Hospital, Linkou|
|Tao Yuan, Taiwan, 333|
|Principal Investigator:||Jiun-Jie Wang, PhD||ChangGung University|