Proc. SPIE. 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
KEYWORDS: Data modeling, Visualization, Control systems, Feature extraction, Genetics, Neuroimaging, Data integration, Functional magnetic resonance imaging, Simulation of CCA and DLA aggregates, Mental disorders, Brain
In the study of complex mental disorders like schizophrenia (SZ), while imaging genetics has achieved great success, imaging epigenetics is attracting increasing attention as it considers the impact of environmental factors on gene expression and resulting phenotypic changes. In this study, we aimed to fill the gap by jointly analyzing imaging and epigenetics data to study SZ. More specifically, we proposed a novel structure-enforced collaborative regression model (SCoRe) to extract co-expressed discriminative features related to SZ from fMRI and DNA methylation data. SCoRe can utilize phenotypical information while enforce an agreement between multiple data views. Moreover, it also considers the group structure within each view of data. The brain network based on fMRI data can be divided into 116 regions of interests (ROIs) based on anatomical structures of the brain and the DNA methylation data can be grouped based on pathway information, which are used as prior knowledge to be incorporated into the learning model. After validation through simulation test, we applied the model to SZ study with data collected by MIND Clinical Imaging Consortium (MCIC). Through integrating fMRI and DNA methylation data of 184 participants (104 SZ and 80 healthy subjects), we succeeded in identifying 8 important brain regions and 3 genes associated with SZ. This study can shed light on the understanding of SZ from both brain imaging and epigenomics, complementary to imaging genomics.