Multimodal classification of Alzheimer's disease and mild cognitive impairment

► We propose to combine MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. ► A high accuracy of 93.2% for AD classification and a high sensitivity of 91.5% (for MCI converters) for MCI classification. ► Each modality is indispensable for achieving good classification. ► CSF and PET have the highest complementary information and MRI and PET have the highest similar information for classification.