title: Parallel ICA with multiple references: a semi-blind multivariate approach.
abstract: High data dimensionality poses a major challenge for imaging genomic studies. To address this issue, a semi-blind multivariate approach, parallel independent component analysis with multiple references (pICA-MR), is proposed. pICA-MR extracts imaging and genetic components in parallel and enhances inter-modality correlations. Prior knowledge is incorporated to emphasize genetic factors with specific attributes. Particularly, pICA-MR can investigate multiple genetic references to explore functional interactions among genes. Simulations demonstrate robust performances with Euclidean distance employed as a metric for reference similarity, where components pointed by the same references are reliably identified and the detection power is significantly improved compared to blind methods.
authors.forename: Jiayu
authors.initials: J
authors.lastname: Chen
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
authors.forename: Alvaro E
authors.initials: AE
authors.lastname: Ulloa
authors.forename: Jingyu
authors.initials: J
authors.lastname: Liu
mesh_terms: D003198:Computer Simulation; D056726:Genetic Association Studies; D006801:Humans; D015810:Linkage Disequilibrium; D008279:Magnetic Resonance Imaging; D008957:Models, Genetic; D015999:Multivariate Analysis; D020641:Polymorphism, Single Nucleotide; D012680:Sensitivity and Specificity; D013223:Statistics as Topic