Identify Consistent Cross-Modality Imaging Genetic Patterns via Discriminant Sparse Canonical Correlation Analysis

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 18; no. 4; pp. 1549 - 1561
• Main Authors: Wang, Meiling, Shao, Wei, Hao, Xiaoke, Shen, Li, Zhang, Daoqiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aged; Aged, 80 and over; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - genetics; Alzheimer's disease; Association analysis; Biomarkers; Brain; Brain - diagnostic imaging; Canonical Correlation Analysis; Correlation; Correlation analysis; Data mining; Diseases; Empirical analysis; Female; Genetics; Genotypes; Humans; Imaging; Imaging genetics; Imaging Genomics - methods; Information processing; Machine learning; Male; Medical imaging; multi-modality; multi-SNP; Multimodal Imaging - methods; Neurodegenerative diseases; Neuroimaging; Phenotypes; Polymorphism, Single Nucleotide - genetics; Similarity; Single-nucleotide polymorphism; sparse canonical correlation analysis; Sparse matrices
• ISSN: 1545-5963; 1557-9964; 1557-9964
• DOI: 10.1109/TCBB.2019.2944825

Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. However, the traditional SCCA algorithm has been designed to seek a linear correlation between the SNP genotype and brain imaging phenotype, ignoring the discriminant similarity information between within-class subjects in brain imaging genetics association analysis. In addition, multi-modality brain imaging phenotypes are extracted from different perspectives and imaging markers from the same region consistently showing up in multimodalities may provide more insights for the mechanistic understanding of diseases. In this paper, a novel multi-modality discriminant SCCA algorithm (MD-SCCA) is proposed to overcome these limitations as well as to improve learning results by incorporating valuable discriminant similarity information into the SCCA algorithm. Specifically, we first extract the discriminant similarity information between within-class subjects by the sparse representation. Second, the discriminant similarity information is enforced within SCCA to construct a discriminant SCCA algorithm (D-SCCA). At last, the MD-SCCA algorithm is adopted to fully explore the relationships among different modalities of different subjects. In experiments, both synthetic dataset and real data from the Alzheimer's Disease Neuroimaging Initiative database are used to test the performance of our algorithm. The empirical results have demonstrated that the proposed algorithm not only produces improved cross-validation performances but also identifies consistent cross-modality imaging genetic biomarkers.