KEYWORDS: Functional magnetic resonance imaging, Brain, Data modeling, Control systems, Simulation of CCA and DLA aggregates, Feature extraction, Visualization, Neuroimaging, Mental disorders, Genetics, Data integration
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.
KEYWORDS: Functional magnetic resonance imaging, Brain, Data modeling, Neuroimaging, Cognitive modeling, Simulation of CCA and DLA aggregates, Biological research, Analytical research, Canonical correlation analysis
Task-based fMRI has been widely studied to investigate individual behavioral and cognitive traits. Integrating multiparadigm fMRI has been proven powerful in analyzing brain development, where a variety of multi-view learning methods have been developed. Among them, collaborative regression (CoRe) combines linear regression with canonical correlation analysis (CCA) to jointly analyze multiple views of data. CoRe links multi-paradigm imaging data with phenotypical information while also enforces their agreement across multiple views. However, CoRe overlooks group structures within regions of interest (ROIs) within the brain. To address this, we proposed a novel model, namely structure-enforced collaborative regression (SCoRe), to take advantage of group structures within each view of fMRI. The model was obtained by imposing a sparse group LASSO penalty on the regression term. Our model was validated on the Philadelphia Neurodevelopmental Cohort dataset by combining multi-task fMRI data to study an individual’s cognitive skills. Specifically, we adopted Wide Range Assessment Test 4 (WRAT4) scores to divide 338 participants into two groups (limited and proficient cognitive skill) and applied SCoRe to identify significant brain regions that can separate them. Through data resampling and significance analysis, we identified 17 brain regions from two paradigms of fMRI as biomarkers associated with an individual’s academic ability. Among them, 5 ROIs are shared by both paradigms. The study may also help understand the mechanisms underlying brain development.
Integration of imaging and non-imaging data has been a heated topic in biomedicine. While functional magnetic resonance imaging (fMRI) can serve as endo-phenotype for mental disorders, many recent researches have confirmed the essential role played by epigenetic factors in the progress of various mental diseases including Schizophrenia(SZ), which fosters an emerging branch imaging epigenetics. In this study, we focus on the integration of fMRI and DNA methylation to have a deeper understanding of SZ: we applied a model combining Lasso with Canonical Correlation Analysis (CCA) for joint DNA methylation and fMRI analysis of 184 subjects (80 patients,104 healthy controls). In the model, the regression term focuses on extracting the discriminative features associated with the disease, while the CCA term incorporates the co-expression among extracted features.We succeeded in
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