TY - JOUR
T1 - Biomarker Identification through Integrating fMRI and Epigenetics
AU - Bai, Yuntong
AU - Pascal, Zille
AU - Hu, Wenxing
AU - Calhoun, Vince D.
AU - Wang, Yu Ping
N1 - Publisher Copyright:
© 1964-2012 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
KW - Imaging epigenetics
KW - canonical correlation analysis
KW - collaborative learning
KW - feature selection
KW - schizophrenia
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U2 - 10.1109/TBME.2019.2932895
DO - 10.1109/TBME.2019.2932895
M3 - Article
C2 - 31395533
AN - SCOPUS:85082342557
SN - 0018-9294
VL - 67
SP - 1186
EP - 1196
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 4
M1 - 8786218
ER -