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Background: Beta-amyloid (A$\beta$) plaques and tau protein tangles in thebrain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), andtogether with structural atrophy detectable on brain magnetic resonance imaging(MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the ''ATNframework'' of AD. Current methods to detect A$\beta$/tau pathology includecerebrospinal fluid (CSF; invasive), positron emission tomography (PET; costlyand not widely available), and blood-based biomarkers (BBBM; promising butmainly still in development).  Objective: To develop a non-invasive and widely available structuralMRI-based framework to quantitatively predict the amyloid and tau measurements.  Methods: With MRI-based hippocampal multivariate morphometry statistics (MMS)features, we apply our Patch Analysis-based Surface Correntropy-induced Sparsecoding and max-pooling (PASCS-MP) method combined with the ridge regressionmodel to individual amyloid/tau measure prediction.  Results: We evaluate our framework on amyloid PET/MRI and tau PET/MRIdatasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Eachsubject has one pair consisting of a PET image and MRI scan, collected at aboutthe same time. Experimental results suggest that amyloid/tau measurementspredicted with our PASCP-MP representations are closer to the real values thanthe measures derived from other approaches, such as hippocampal surface area,volume, and shape morphometry features based on spherical harmonics (SPHARM).  Conclusion: The MMS-based PASCP-MP is an efficient tool that can bridgehippocampal atrophy with amyloid and tau pathology and thus help assess diseaseburden, progression, and treatment effects.

Improved Prediction of Beta-Amyloid and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding