Machine learning for prediction of pathology based on neuropsychological
performance and lifestyle data 
   Lead Investigator:    Thanaphong Phongpreecha
   Institution      :    Stanford University
   E-Mail           :    tpjoe@stanford.edu
   Proposal ID      :    1671



   Proposal Description:
   The hypothesis we want to test is if there is a pattern in neuropsychological  performance and lifestyle data that could be leveraged by machine learning  models to predict pathological features, particularly neuropathology such as  Braak scores or CERAD. If successful, this research would be impactful because  it would establish a relationship, and hence prediction, between features that  are measurable when the patients are still alive to the true neuropathology of  Alzheimer's disease that could only be obtained from autopsy. As a secondary  goal, we would also aim to establish a relationship between neuropsychological  and lifestyle data to the biomarker values (CSF and MRI).