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<B>Integration of multi modal data to predict progression to AD
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   Lead Investigator:    Benoit Lehallier
   Institution      :    Stanford University
   E-Mail           :    lehallib@stanford.edu
   Proposal ID      :    617
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   Proposal Description:
   <textarea name="description" rows="12" cols="85" wrap="virtual" readonly>We would like to test the hypothesis that in addition to known                    neuropathological changes in the brain and cerebrospinal fluid (CSF),genetic      and other alterations are also associated with progression from cognitively       normal or Mild Cognitive Impairment (MCI) state to Alzheimer???s Disease (AD)     dementia. Our approach aims to find a multimodal combination of biomarkers that   would predict progression from to AD. A major originality of this approach is     to perform a comprehensive screening of all available biomarkers without a        priori of the biomarker category (e.g. not only MRI biomarkers etc). We           identified in the ADNI dataset a candidate signature that could be used for       early diagnosis or monitoring the disease progression. We will use the NACC       dataset to try to validate this signature. In addition, we expect to find         genetic variants (SNPs) associated with this signature to AD.                                                                                                       Aim 1: To find a multimodal combination of biomarker to predict progression to    AD in the NACC dataset and to estimate its overlap with the one we identified     in the ADNI cohort                                                                                                                                                  Aim 2: To identify possible new genetic variants associated with the signature    to AD </textarea>