Identification of Discriminative Imaging Proteomics Associations in Alzheimer’s Disease Via a Novel Sparse Correlation Model
Year: 2016
Type: conference-paper
Abstract: Brain imaging and protein expression, from both cerebrospinal fluid and blood plasma, have been found to provide complementary information in predicting the clinical outcomes of Alzheimer's disease (AD). But the underlying associations that contribute to such a complementary relationship have not been previously studied yet. In this work, we will perform an imaging proteomics association analysis to explore how they are related with each other. While traditional association models, such as Sparse Canonical Correlation Analysis (SCCA), can not guarantee the selection of only disease-relevant biomarkers and associations, we propose a novel discriminative SCCA (denoted as DSCCA) model with new penalty terms to account for the disease status information. Given brain imaging, proteomic and diagnostic data, the proposed model can perform a joint association and multi-class discrimination analysis, such that we can not only identify disease-relevant multimodal biomarkers, but also reveal strong associations between them. Based on a real imaging proteomic data set, the empirical results show that DSCCA and traditional SCCA have comparable association performances. But in a further classification analysis, canonical variables of imaging and proteomic data obtained in DSCCA demonstrate much more discrimination power toward multiple pairs of diagnosis groups than those obtained in SCCA.
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Awards: G104586243, Indiana Alzheimer Disease Center, Bioinformatics Strategies for Multidimensional Brain Imaging Genetics, Indiana Clinical and Translational Sciences Institute, Sensory and Perceptual Measures as Biomarkers of Alzheimer's Disease Pathology, Integration of Bio-, Medical, and Imaging Informatics for Complex Diseases, Integrative Bioinformatics Approaches to Human Brain Genomics and Connectomics, Alzheimer's Disease Neuroimaging Initiative, Integrating Neuroimaging, Multi-omics, and Clinical Data in Complex Disease, Memory Circuitry in MCI and Early Alzheimerâs Disease Prodrome: Molecular Drivers
OpenAlex ID: W2554641909