Abstract
INTRODUCTION: Amyloid beta (Aβ) plaques and hyperphosphorylated tau in the entorhinal regions are key Alzheimer's disease (AD) markers, but the spatial Aβ pathways influencing tau pathology remain unclear. METHODS: We applied predictive modeling to identify Aβ standardized uptake value ratio (SUVR) spatial patterns that predict entorhinal tau levels, future hippocampal volume, and Preclinical Alzheimer's Cognitive Composite (PACC) scores at 5-year follow-up. The model was trained on Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 237), incorporating amyloid-PET (positron emission tomography), tau-PET, magnetic resonance imaging (MRI), and cognitive data, and validated on Harvard Aging Brain Study (HABS) (N = 276). RESULTS: The model accurately predicted entorhinal tau levels (r = 0.48, p < 0.0001), future hippocampal volume (r = 0.24, p = 0.002), and PACC scores (r = 0.35, p < 0.0001) based on regional Aβ. DISCUSSION: Aβ in the rostral middle frontal, medial orbitofrontal, and striatal regions predict entorhinal tau levels, future hippocampal volume, and PACC scores, indicating their potential as early biomarkers in AD prediction models. HIGHLIGHTS: Positron emission tomography (PET) imaging reveals amyloid beta (Aβ) patterns predicting entorhinal tau levels in preclinical Alzheimer's disease (AD). Aβ in medial orbitofrontal, rostral middle frontal, and nucleus accumbens best predicts tau. Aβ distribution in these regions predicts future hippocampal neurodegeneration and cognitive decline. Model validated with Alzheimer's Disease Neuroimaging Initiative (ADNI) and Harvard Aging Brain Study (HABS) data sets, showing robustness and reproducibility. Findings suggest early Aβ patterns can aid in diagnosing AD and guide anti-Aβ therapies.