Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function

Amyloid-beta plaque is a neuropathological hallmark of Alzheimer’s disease (AD). As anti-amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility. The goal of this study is to predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer’s Disease and Cognitive Aging (ARMADA) study. ARMADA is a multisite study that implemented the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive ability levels (normal, mild cognitive impairment, early-stage dementia of the AD type). 199 ARMADA participants had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 33.2% with positive AD biomarkers). We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status. The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI: 0.73–0.76, Sensitivity 0.50, Specificity 0.88); higher than the LASSO model (0.68). The 10 features with the highest importance included picture sequence memory, cognition composites, words-in-noise test, processing speed, odor identification, and 2-minute walk endurance. Our results support the utilization of the NIH Toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid-negative than amyloid-positive cases.