Machine learning prediction of future amyloid beta positivity in amyloid-negative individuals

Abstract

INTRODUCTIONThe pathophysiology of Alzheimers disease (AD) involves {beta}-amyloid (A{beta}) accumulation. Early identification of individuals with abnormal {beta}-amyloid levels is crucial, but A{beta} quantification with positron emission tomography (PET) and cerebrospinal fluid (CSF) is invasive and expensive. METHODSWe propose a machine learning framework using standard non-invasive (MRI, demographics, APOE, neuropsychology) measures to predict future A{beta}-positivity in A{beta}-negative individuals. We separately study A{beta}-positivity defined by PET and CSF. RESULTS: Cross-validated AUC for 4-year A{beta} conversion prediction was 0.78 for the CSF-based and 0.68 for the PET-based A{beta} definitions. Although not trained for the clinical status-change prediction, the CSF-based model excelled in predicting future mild cognitive impairment (MCI)/dementia conversion in cognitively normal/MCI individuals (AUCs, respectively, 0.76 and 0.89 with a separate dataset). DISCUSSIONStandard measures have potential in detecting future A{beta}-positivity and assessing conversion risk, even in cognitively normal individuals. The CSF-based definition led to better predictions than the PET-based definition.