Biomarker Identification Through Integrating fMRI and Epigenetics

Abstract

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 [Formula: see text]. 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.

Figure 1:

Results on synthetic toy dataset with our MT-CoReg model. Each graph represents one view where blue and orange lines correspond to the simulation settings, and green bars correspond to the selected features ($|\alpha |+|\theta |$ value).

Figure 2:

Visualization of feature selection results after stable t-test on fMRI. From left to right: (a) sagittal view, (b) axial view, (c) coronal view. Each node corresponds a brain region defined in AAL template. The size of the node represents the sum of frequency of the corresponding voxels. The color of the node represents the adjusted average frequency of corresponding voxels. The visualization is by BrainNet Viewer.

Figure 3:

Flow chart of one run of experiment. During each run of experiment, the feature selection result will differ because of the data permutation. To reduce this type of bias, we repeat the process by resampling the raw data. After multiple runs of experiments, a significance test on βs is conducted to detect the biomarkers.

Figure 4:

Visualization of the 14 ROIs that have no less than 2 selected voxels. The visualization is by the BrainNet Viewer toolbox. From left to right: (a) sagittal view, (b) axial view, (c) coronal view.

Figure 5:

The ROC curve of classification using SVM to classify SZ and HC with extracted features, concatenation of DNA methylation and fMRI data, and each individual data. The x axis corresponds to FPR value, and the y axis is the TPR value. The closer the curve to the top left, the better. The AUC is 0.8951 for using extracted features, 0.7875 for using methylation, 0.6497 for using fMRI data, and 0.8125 for using the concatenation of raw data.

Figure 6:

The correlation heatmaps between the selected voxels (rows) and CpG sites(columns). Left: correlation of features selected by MT-CoReg; right: correlation of features selected by Lasso. Y axis displays the corresponding brain regions defined by AAL template; and x axis displays the corresponding gene names. It is clear that the heatmap calculated from MT-CoReg has much higher absolute values compared to the one calculated from Lasso. Furthermore, the left heatmap is more structured than the right one and the blocks of same color (blue or red) tend to form clusters.

Figure 7:

Visualization of biological interactions in the Pathways ”Guanosine nucleotides de novo biosynthesis” (subfigure a) and ”Guanosine ribonucleotides de novo biosynthesis” (subfigure b).

Figure 8:

The Venn diagram to demonstrate the brain regions identified using CoReg on fMRI-SNP study (red) and on fMRI-Methylation study (green). There are two regions identified in both studies: left middle occipital gyrus and right parahippocampal gyrus. Brain regions are defined by AAL template.