Bayesian network model  on reversion and post-reversion of mild cognitive
impairment to normal cognition 


   Note: An Official Proposal has not been Submitted to NACC.


   Lead Investigator:    Yao Qin
   Institution      :    Shanxi Medical University
   E-Mail           :    yaooqin@163.com
   Proposal ID      :    1683



   Proposal Description:
   Mild cognitive impairment (MCI) is generally considered to be an intermediate state between normal aging and Alzheimer's disease (AD). Currently, a large number of studies have regarded MCI as a high-risk population of AD and a therapeutic target to predict its progression to AD. Although approximately 10 to 15 annual risk of progressing to AD among MCI, cognitive trajectories of MCI are highly heterogeneous and most MCI do not progress to AD over time. The tendency to treat MCI solely as a prodrome of dementia has led to an unbalanced study. If follow-up is long enough, will normal cognitive ability be maintained after the reversion from MCI to NC or will it progress further to MCI or AD? Up to now, the specific mechanisms of the reversion from MCI to NC are still very limited. Existing studies have only used as a reference for the progress of MCI, and controversies significance of the reversion from MCI to NC has not been determined yet. Based on the characteristics of multiple state, influence factors and the diversity of two-way outcomes for MCI (reversion, stability and progression), we plan to use the public databases (NACC) to build Bayesian network model of the reversion from MCI to NC, to explore the transition probability of each state of reversion and post-reversion outcome between various factors, so as to realize the mechanism interpretation, prediction, monitoring and prevention of MCI reversion to NC and post-reversion.