  Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may
be the key to prevention breakthroughs. One of the hallmarks of AD is the
accumulation of tau plaques in the human brain. However, current methods to
detect tau pathology are either invasive (lumbar puncture) or quite costly and
not widely available (Tau PET). In our previous work, structural MRI-based
hippocampal multivariate morphometry statistics (MMS) showed superior
performance as an effective neurodegenerative biomarker for preclinical AD and
Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling
(PASCS-MP) has excellent ability to generate low-dimensional representations
with strong statistical power for brain amyloid prediction. In this work, we
apply this framework together with ridge regression models to predict Tau
deposition in Braak12 and Braak34 brain regions separately. We evaluate our
framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative
(ADNI). Each subject has one pair consisting of a PET image and MRI scan which
were collected at about the same times. Experimental results suggest that the
representations from our MMS and PASCS-MP have stronger predictive power and
their predicted Braak12 and Braak34 are closer to the real values compared to
the measures derived from other approaches such as hippocampal surface area and
volume, and shape morphometry features based on spherical harmonics (SPHARM).
