The purpose of this study is to find the characteristics of mild cognitive impairment (MCI) using technology of Multi-Modality MRI , including structural MRI, functional MRI and diffusion tensor imaging(DTI). Then analyze the difference between progressive MCI (MCIp) and stable MCI (MCIs) and further construct the predictable classifier from MCI to Alzheimer's disease (AD) based on Multi-Modality MRI characteristics of MCI patients.
The cognition of MCI is between normal healthy and AD, which is thought the transitional stage of AD. Patients with MCI have heavy risk to convert to AD, so in this study, the investigators focus on the exploration of the characteristics of mild cognitive impairment (MCI) using technology of Multi-Modality MRI, including structural MRI, functional MRI and DTI. Then the investigators further study the patients who convert to AD and explore their MRI characteristics on baseline, in order to construct the predictable classifier from MCI to AD. The investigators want to achieve the early diagnosis of AD and help clinicians interfere with the progress of this disease.
Study Type
OBSERVATIONAL
Enrollment
150
Department of Neurolgy,Xuanwu Hospital of Capital Medical University
Beijing, Beijing Municipality, China
the accuracy of a predictable classifier from MCI to AD based on Multi-Modality MRI characteristics of MCI patients.
one-hundred MCI subjects and 50 healthy controls recruited underwent structure,resting-state functional magnetic resonance imaging and diffusion tensor imaging.After 2-year follow-up,the MCI subjects were divided into progressive MCI(MCIp) and stable MCI(MCIs).Based on differences among MCIp，MCIs and healthy controls in baseline neuroimaging data，some suitable indicators were selected，and then a predictable classifier from MCI to AD based on multi-modality MRI was constructed.At last，the leave-one-out cross validation analysis were conducted to estimate the accuracy of the classifier.The classification accuracy was measured by the proportion of MCI subjects that were correctly classified into the MCIp or MCIs groups
Time frame: 2 years
characteristic changes of brain structure in progressive MCI
voxel based morphometry (VBM) and cortical-thicknessanalysis（CTA）based on structural MRI were used to characterize the changes of brain structure in the MCIp comparing with the MCIs and healthy control
Time frame: 2 years
characteristic changes of anatomical connectivity in progressing MCI
region of interest(ROI),voxel and fiber bundle analysis based on diffusion tensor imaging were used to detect differences among the MCIp，MCIs and healthy control in fractional anisotropy(FA) and mean diffusivity(MD).
Time frame: 2 years
characteristic changes of functional connectivity in progressing MCI
functional connectivity（FC） was compared among the MCIp, MCIs and healthy control using resting-state functional magnetic resonance imaging.
Time frame: 2 years
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