Structural magnetic resonance imaging (sMRI) is an established technique for measuring brain atrophy, and dynamic positron emission tomography with (11)C-Pittsburgh compound B ((11)C-PIB PET) has the potential to provide both perfusion and amyloid deposition information. It remains unclear, however, how to better combine perfusion, amyloid deposition and morphological information extracted from dynamic (11)C-PIB PET and sMRI with the goal of improving the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). We adopted a linear sparse support vector machine to build classifiers for distinguishing AD and MCI subjects from cognitively normal (CN) subjects based on different combinations of regional measures extracted from imaging data, including perfusion and amyloid deposition information extracted from early and late frames of (11)C-PIB separately, and gray matter volumetric information extracted from sMRI data. The experimental results demonstrated that the classifier built upon the combination of imaging measures extracted from early and late frames of (11)C-PIB as well as sMRI achieved the highest classification accuracy in both classification studies of AD (100%) and MCI (85%), indicating that multimodality information could aid in the diagnosis of AD and MCI.
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