Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity

Current developments of neuroimaging and genetics promote an integrative and compressive study of schizophrenia. However, it is still difficult to explore how gene mutations are related to brain abnormalities due to the high dimension but low sample size of these data. Conventional approaches reduce the dimension of dataset separately and then calculate the correlation, but ignore the effects of the response variables and the structure of data. To improve the identification of risk genes and abnormal brain regions on schizophrenia, in this paper, we propose a novel method called Independence and Structural sparsity Canonical Correlation Analysis (ISCCA). ISCCA combines independent component analysis (ICA) and Canonical Correlation Analysis (CCA) to reduce the collinear effects, which also incorporate graph structure of the data into the model to improve the accuracy of feature selection. The results from simulation studies demonstrate its higher accuracy in discovering correlations compared with other competing methods. Moreover, applying ISCCA to a real imaging genetics dataset collected by Mind Clinical Imaging Consortium (MCIC), a set of distinct gene-ROI interactions are identified, which are verified to be both statistically and biologically significant.