Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


BACKGROUND: Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. OBJECTIVE: The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aβ+ based on gold-standard of PET imaging. METHODS: We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aβ status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers. RESULTS: The models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and MRI measures jointly in the model did not improve prediction. The models including CSF biomarkers significantly outperformed other models with AUCs between 0.89 to 0.92. CONCLUSIONS: Predictive models can be effectively used to identify persons with aMCI likely to be amyloid positive on a subsequent PET scan.

Original languageEnglish (US)
Pages (from-to)1211-1219
Number of pages9
JournalJournal of Alzheimer's disease : JAD
Issue number3
StatePublished - 2020


  • Alzheimer’s disease
  • amyloid imaging
  • machine learning
  • mild cognitive impairment
  • predictive analytics

ASJC Scopus subject areas

  • General Neuroscience
  • Clinical Psychology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health


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