Identify Biomarkers of Alzheimer's Disease Based on Multi-task Canonical Correlation Analysis and Regression Model.

Imaging genetics using imaging technology is regarded as a neuroanatomical phenotype to evaluate gene single nucleotide polymorphisms and their effects on the structure and function of different brain regions. It plays a vital role in bridging the initial understanding of the genetic basis of brain structure and dysfunction. Sparse canonical correlation analysis (SCCA) has become a widespread technique in this field because of its powerful ability to identify bivariate relationships and feature selection. Since most traditional SCCA algorithms assume that the input features are independent, this method obviously cannot be used to analyze genetic image data. The MT-SCCA model is unsupervised and cannot identify the genotype-phenotype associations for diagnostic guidance. Meanwhile, a single biological clinical index cannot fully reflect the physiological process of a comprehensive disease. Therefore, it is necessary to find biomarkers that can reflect Alzheimer's disease and physiological functions that can more comprehensively reflect the development of the disease. This article uses a multi-task sparse canonical correlation analysis and regression (MT-SCCAR) model to combine the annual depression level total score (GDSCALE), clinical dementia assessment scale (GLOBAL CDR), functional activity questionnaire (FAQ), and neuropsychiatric Symptom Questionnaire (NPI-Q) in this paper. These four clinical data are used as compensation information and embedded in the algorithm in a linear regression manner. It also reflects its superiority and robustness compared to traditional correlation analysis methods on actual and simulated data. Meanwhile, compared with MT-SCCA, the model utilized in this paper obtains a higher gene-ROI weight and identifies clearer biomarkers, which provides a practical basis for the study of complex human disease pathology.