Joint-Connectivity-Based Sparse Canonical Correlation Analysis of Imaging Genetics for Detecting Biomarkers of Parkinson's Disease

• Published in: IEEE transactions on medical imaging Vol. 39; no. 1; pp. 23 - 34
• Main Authors: Kim, Mansu, Won, Ji Hye, Youn, Jinyoung, Park, Hyunjin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomarkers; Brain; brain connectivity; Computer simulation; Connectivity; Correlation; Correlation analysis; Datasets; Diseases; Genetics; Imaging genetics; Magnetic resonance imaging; magnetic resonance imaging (MRI); Medical imaging; Movement disorders; Neural networks; Neurodegenerative diseases; Neuroimaging; Neurological diseases; Nucleotides; Parkinson's disease; Parkinson’s disease (PD); Performance evaluation; prior information; single nucleotide polymorphism (SNP); Single-nucleotide polymorphism; sparse canonical correlation analysis (SCCA); Sparse matrices

Imaging genetics is a method used to detect associations between imaging and genetic variables. Some researchers have used sparse canonical correlation analysis (SCCA) for imaging genetics. This study was conducted to improve the efficiency and interpretability of SCCA. We propose a connectivity-bas...