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Updated: Jul 2, 2025
Detecting Amyloid-β Accumulation via Immunofluorescent Staining in a Mouse Model of Alzheimer's Disease
Published on: April 19, 2021
Machine learning prediction of future amyloid beta positivity in amyloid-negative individuals
Elaheh Moradi1, Mithilesh Prakash2, Anette Hall3,4
1A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70150, Finland. elaheh.moradi@uef.fi.
Background:
The pathophysiology of Alzheimer's disease (AD) involves -amyloid (A ) accumulation. Early identification of individuals with abnormal -amyloid levels is crucial, but A quantification with positron emission tomography (PET) and cerebrospinal fluid (CSF) is invasive and expensive.
Methods:
We propose a machine learning framework using standard non-invasive (MRI, demographics, APOE, neuropsychology) measures to predict future A -positivity in A -negative individuals. We separately study A -positivity defined by PET and CSF.
Results:
Cross-validated AUC for 4-year A conversion prediction was 0.78 for the CSF-based and 0.68 for the PET-based A 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).
Conclusion:
Standard measures have potential in detecting future A -positivity and assessing conversion risk, even in cognitively normal individuals. The CSF-based definition led to better predictions than the PET-based definition.
