title: DIAGNOSIS-GUIDED METHOD FOR IDENTIFYING MULTI-MODALITY NEUROIMAGING BIOMARKERS ASSOCIATED WITH GENETIC RISK FACTORS IN ALZHEIMER'S DISEASE.
abstract: Many recent imaging genetic studies focus on detecting the associations between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs). Although there exist a large number of generalized multivariate regression analysis methods, few of them have used diagnosis information in subjects to enhance the analysis performance. In addition, few of models have investigated the identification of multi-modality phenotypic patterns associated with interesting genotype groups in traditional methods. To reveal disease-relevant imaging genetic associations, we propose a novel diagnosis-guided multi-modality (DGMM) framework to discover multi-modality imaging QTs that are associated with both Alzheimer's disease (AD) and its top genetic risk factor (i.e., APOE SNP rs429358). The strength of our proposed method is that it explicitly models the priori diagnosis information among subjects in the objective function for selecting the disease-relevant and robust multi-modality QTs associated with the SNP. We evaluate our method on two modalities of imaging phenotypes, i.e., those extracted from structural magnetic resonance imaging (MRI) data and fluorodeoxyglucose positron emission tomography (FDG-PET) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results demonstrate that our proposed method not only achieves better performances under the metrics of root mean squared error and correlation coefficient but also can identify common informative regions of interests (ROIs) across multiple modalities to guide the disease-induced biological interpretation, compared with other reference methods.
authors.affiliation: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China2Department of Radiology & Imaging Science, School of Medicine, Indiana University, Indianapolis, 46202, USA, robinhc@163.com.
authors.forename: Xiaoke
authors.initials: X
authors.lastname: Hao
authors.forename: Jingwen
authors.initials: J
authors.lastname: Yan
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Yao
authors.forename: Shannon L
authors.initials: SL
authors.lastname: Risacher
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Saykin
authors.forename: Daoqiang
authors.initials: D
authors.lastname: Zhang
authors.forename: Li
authors.initials: L
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000235:genetics*; D001057:Apolipoproteins E / Q000235:genetics; D015415:Biomarkers; D001921:Brain / Q000473:pathology; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D016208:Databases, Factual / Q000706:statistics & numerical data; D056726:Genetic Association Studies; D006801:Humans; D008279:Magnetic Resonance Imaging; D064847:Multimodal Imaging; D059906:Neuroimaging; D020641:Polymorphism, Single Nucleotide; D049268:Positron-Emission Tomography; D019655:Quantitative Trait, Heritable; D012307:Risk Factors
chemical_list: D001057:Apolipoproteins E; D015415:Biomarkers