Multi-modal classification of Alzheimer's disease using nonlinear graph fusion

Accurate diagnosis of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI) is of great interest to patients and clinicians. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis and prognosis. Classification methods are needed to combine these multiple biomarkers to provide an accurate diagnosis. State-of-the-art approaches calculate a mixed kernel or a similarity matrix by linearly combining kernels or similarities from multiple modalities. However, the complementary information from multi-modal data are not necessarily linearly related. In addition, this linear combination is also sensitive to the weights assigned to each modality. In this paper, we present a multi-modality classification framework to efficiently exploit the complementarity in the multi-modal data. First, pairwise similarity is calculated for each modality individually using the features including regional MRI volumes, voxel-based FDG-PET signal intensities, CSF biomarker measures, and categorical genetic information. Similarities from multiple modalities are then combined in a nonlinear graph fusion process, which generates a unified graph for final classification. Based on the unified graphs, we achieved classification area under curve (AUC) of receiver-operator characteristic of 98.1% between AD subjects and normal controls (NC), 82.4% between MCI subjects and NC and 77.9% in a three-way classification, which are significantly better than those using single-modality biomarkers and those based on state-of-the-art linear combination approaches.