  The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment
(MCI) associated with brain changes remains a challenging task. Recent studies
have demonstrated that combination of multi-modality imaging techniques can
better reflect pathological characteristics and contribute to more accurate
diagnosis of AD and MCI. In this paper, we propose a novel tensor-based
multi-modality feature selection and regression method for diagnosis and
biomarker identification of AD and MCI from normal controls. Specifically, we
leverage the tensor structure to exploit high-level correlation information
inherent in the multi-modality data, and investigate tensor-level sparsity in
the multilinear regression model. We present the practical advantages of our
method for the analysis of ADNI data using three imaging modalities (VBM- MRI,
FDG-PET and AV45-PET) with clinical parameters of disease severity and
cognitive scores. The experimental results demonstrate the superior performance
of our proposed method against the state-of-the-art for the disease diagnosis
and the identification of disease-specific regions and modality-related
differences. The code for this work is publicly available at
https://github.com/junfish/BIOS22.
