Biomarker Identification Through Integrating fMRI and Epigenetics

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 4; pp. 1186 - 1196
• Main Authors: Bai, Yuntong, Pascal, Zille, Hu, Wenxing, Calhoun, Vince D., Wang, Yu-Ping
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biomarkers; Biosynthesis; Brain; Brain mapping; canonical correlation analysis; collaborative learning; Consortia; Correlation; Correlation analysis; CpG islands; Cytosine; Deoxyribonucleic acid; DNA; DNA Methylation; Epigenesis, Genetic - genetics; Epigenetics; Feature extraction; feature selection; Functional magnetic resonance imaging; Genes; Genetics; Guanine; Guanosine; Humans; Identification methods; Imaging epigenetics; Linear regression; Magnetic Resonance Imaging; Medical imaging; Mental disorders; Nervous system; Neuroimaging; Nucleotides; Regression analysis; Ribonucleotides; Schizophrenia; Schizophrenia - diagnostic imaging; Schizophrenia - genetics
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2019.2932895

Objective: Integration of multiple datasets is a hot topic in many fields. When studying complex mental disorders, great effort has been dedicated to fusing genetic and brain imaging data. However, an increasing number of studies have pointed out the importance of epigenetic factors in the cause of psychiatric diseases. In this study, we endeavor to fill the gap by combining epigenetics (e.g., DNA methylation) with imaging data (e.g., fMRI) to identify biomarkers for schizophrenia (SZ). Methods: We propose to combine linear regression with canonical correlation analysis (CCA) in a relaxed yet coupled manner to extract discriminative features for SZ that are co-expressed in the fMRI and DNA methylation data. Result: After validation through simulations, we applied our method to real imaging epigenetics data of 184 subjects from the Mental Illness and Neuroscience Discovery Clinical Imaging Consortium. After significance test, we identified 14 brain regions and 44 cytosine-phosphate-guanine(CpG) sites. Average classification accuracy is \text{88.89}\%. By linking the CpG sites to genes, we identified pathways Guanosine ribonucleotides de novo biosynthesis and Guanosine nucleotides de novo biosynthesis, and a GO term Perikaryon. Conclusion: This imaging epigenetics study has identified both brain regions and genes that are associated with neuron development and memory processing. These biomarkers contribute to a good understanding of the mechanism underlying SZ but are overlooked by previous imaging genetics studies. Significance: Our study sheds light on the understanding and diagnosis of SZ with a imaging epigenetics approach, which is demonstrated to be effective in extracting novel biomarkers associated with SZ.