Structured Sparse Kernel Learning for Imaging Genetics Based Alzheimer’s Disease Diagnosis

A kernel-learning based method is proposed to integrate multimodal imaging and genetic data for Alzheimer’s disease (AD) diagnosis. To facilitate structured feature learning in kernel space, we represent each feature with a kernel and then group kernels according to modalities. In view of the highly redundant features within each modality and also the complementary information across modalities, we introduce a novel structured sparsity regularizer for feature selection and fusion, which is different from conventional lasso and group lasso based methods. Specifically, we enforce a penalty on kernel weights to simultaneously select features sparsely within each modality and densely combine different modalities. We have evaluated the proposed method using magnetic resonance imaging (MRI) and positron emission tomography (PET), and single-nucleotide polymorphism (SNP) data of subjects from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The effectiveness of our method is demonstrated by both the clearly improved prediction accuracy and the discovered brain regions and SNPs relevant to AD.