A modified FGL sparse canonical correlation analysis for the identification of Alzheimer's disease biomarkers

Abstract

Imaging genetics mainly finds the correlation between multiple datasets, such as imaging and genomics. Sparse canonical correlation analysis (SCCA) is regarded as a useful method that can find connections between specific genes, SNPs, and diseased brain regions. Fused pairwise group lasso-SCCA (FGL-SCCA) can discover the chain relationship of genetic variables within the same modality or the graphical relationship between images. However, it can only handle genetic and imaging data from a single modality. As Alzheimer's disease is a kind of complex and comprehensive disease, a single clinical indicator cannot accurately reflect the physiological process of the disease. It is urgent to find biomarkers that can reflect AD and more synthetically reflect the physiological function of disease development. In this study, we proposed a multimodal sparse canonical correlation analysis model FGL-JSCCAGNR combined FGL-SCCA and Joint SCCA (JSCCA) method which can process multimodal data. Based on the JSCCA algorithm, it imposes a GraphNet regularization penalty term and introduces a fusion pairwise group lasso (FGL), and a graph-guided pairwise group lasso (GGL) penalty term, the algorithm in this paper can combine data between different modalities, Finally, the Annual Depression Level Total Score (GDSCALE), Clinical Dementia Rating Scale (GLOBAL CDR), Functional Activity Questionnaire (FAQ) and Neuropsychiatric Symptom Questionnaire (NPI-Q), these four clinical data are embedded in the model by linear regression as compensation information. Both simulation data and real data analysis show that when FGI-JSCCAGNR is applied to the imaging genetics study of Alzheimer's patients, the model presented here can detect more significant genetic variants and diseased brain regions. It provides a more robust theoretical basis for clinical researchers.