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

Abstract

AbstractBackgroundAmyloid‐beta (Ab) plaque is a pathophysiological hallmark of Alzheimer’s disease (AD). As anti‐amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility. Here, we predicted brain amyloid status in the Advancing Reliable Measurement in Alzheimer’s Disease and Cognitive Aging (ARMADA) study using machine learning.MethodsARMADA, a multisite study, aimed to validate the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive abilities (normal, MCI, early AD dementia). We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. Each site used its own criteria to define the amyloid PET positivity (Positive for AD biomarkers, Negative, Visual reading unavailable) or CSF result (Consistent with AD, Not consistent with AD, Borderline, Indeterminate). Amyloid positivity was defined as PET being “Positive for AD biomarkers” or CSF being “Consistent with AD.” We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity.ResultsOf 462 ARMADA participants, 199 had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers). 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) on the held‐out test set; higher than the LASSO model (0.68 (95% CI:0.68 – 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words‐in‐noise test (hearing), pattern comparison processing speed, odor identification, 2‐minutes‐walk endurance, 4‐meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers.ConclusionOur results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive (i.e., low sensitivity) cases.