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Updated: Dec 7, 2025
Full- versus Sub-Regional Quantification of Amyloid-Beta Load on Mouse Brain Sections
Published on: May 19, 2022
Predicting amyloid status using self-report information from an online research and recruitment registry: The Brain
Miriam T Ashford1,2, John Neuhaus3, Chengshi Jin3
1Northern California Institute for Research and Education (NCIRE) Department of Veterans Affairs Medical Center San Francisco California USA.
Introduction:
This study aimed to predict brain amyloid beta (Aβ) status in older adults using collected information from an online registry focused on cognitive aging.
Methods:
Aβ positron emission tomography (PET) was obtained from multiple in-clinic studies. Using logistic regression, we predicted Aβ using self-report variables collected in the Brain Health Registry in 634 participants, as well as a subsample (N = 533) identified as either cognitively unimpaired (CU) or mild cognitive impairment (MCI). Cross-validated area under the curve (cAUC) evaluated the predictive performance.
Results:
The best prediction model included age, sex, education, subjective memory concern, family history of Alzheimer's disease, Geriatric Depression Scale Short-Form, self-reported Everyday Cognition, and self-reported cognitive impairment. The cross-validated AUCs ranged from 0.62 to 0.66. This online model could help reduce between 15.2% and 23.7% of unnecessary Aβ PET scans in CU and MCI populations.
Disucssion:
The findings suggest that a novel, online approach could aid in Aβ prediction.
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