Abstract Imaging genetic studies focus on feature extraction from brain regions-of-interest, but few of them successfully depict the relations among brain areas. It is studied recently that brain properties can be better reflected by adopting a structured network model to quantify the complex connection among brain areas. Accordingly, a hyper-network guided sparse multi-task canonical correlation analysis algorithm is proposed in this paper. Firstly, a sparse representation method is employed and the resting-state fMRI time series are used to construct the hyper-network. Then, three clustering coefficients are extracted from the hyper-network as brain imaging characteristics. Finally, the sparse multi-task canonical correlation analysis is used to acquire the link between genes and three types of image features. The experimental results on ADNI dataset show that the proposed algorithm is helpful to improve the performance of analyzing associations between genotype and phenotype data and discovering some genetic risk factors closely related to the disease.
About author:: (LI Chanxiu, born in 1993, master student. Her research interests include image genetics and machine learning.) (HAO Xiaoke, born in 1985, Ph.D. candidate. His research interests include image genetics and machine learning.) (ZHANG Daoqiang(Corresponding author), born in 1978, Ph.D., professor. His research interests include pattern recognition, neural computing, machine learning and data mining.)
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2017, Vol. 30 
