Browsing by Author "Wan, Jing"

Now showing 1 - 5 of 5
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    Hippocampal Surface Mapping of Genetic Risk Factors in AD via Sparse Learning Models
    (Office of the Vice Chancellor for Research, 2012-04-13) Wan, Jing; Kim, Sungeun; Inlow, Mark; Nho, Kwangsik; Swaminathan, Shanker; Risacher, Shannon L.; Fang, Shiaofen; Weiner, Michael W.; Beg, M. Faisal; Wang, Lei; Saykin, Andrew J.; Shen, Li; ADNI
    Genetic mapping of hippocampal shape, an under-explored area, has strong potential as a neurodegeneration biomarker for AD and MCI. This study investigates the genetic effects of top candidate single nucleotide polymorphisms (SNPs) on hippocampal shape features as quantitative traits (QTs) in a large cohort. FS+LDDMM was used to segment hippocampal surfaces from MRI scans and shape features were extracted after surface registration. Elastic net (EN) and sparse canonical correlation analysis (SCCA) were proposed to examine SNP-QT associations, and compared with multiple regression (MR). Although similar in power, EN yielded substantially fewer predictors than MR. Detailed surface mapping of global and localized genetic effects were identified by MR and EN to reveal multi-SNP-single-QT relationships, and by SCCA to discover multi-SNP-multi-QT associations. Shape analysis identified stronger SNP-QT correlations than volume analysis. Sparse multivariate models have greater power to reveal complex SNP-QT relationships. Genetic analysis of quantitative shape features has considerable potential for enhancing mechanistic understanding of complex disorders like AD.
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    Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net
    (Springer-Verlag, 2011-09) Shen, Li; Kim, Sungeun; Qi, Yuan; Inlow, Mark; Swaminathan, Shanker; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Shaw, Leslie M.; Trojanowski, John Q.; Weiner, Michael W.; Saykin, Andrew J.; Department of Radiology and Imaging Sciences, IU School of Medicine
    Multi-modal neuroimaging and biomarker data provide exciting opportunities to enhance our understanding of phenotypic characteristics associated with complex disorders. This study focuses on integrative analysis of structural MRI data and proteomic data from an RBM panel to examine their predictive power and identify relevant biomarkers in a large MCI/AD cohort. MRI data included volume and thickness measures of 98 regions estimated by FreeSurfer. RBM data included 146 proteomic analytes extracted from plasma and serum. A sparse learning model, elastic net logistic regression, was proposed to classify AD and MCI, and select disease-relevant biomarkers. A linear support vector machine coupled with feature selection was employed for comparison. Combining RBM and MRI data yielded improved prediction rates: HC vs AD (91.9%), HC vs MCI (90.5%) and MCI vs AD (86.5%). Elastic net identified a small set of meaningful imaging and proteomic biomarkers. The elastic net has great power to optimize the sparsity of feature selection while maintaining high predictive power. Its application to multi-modal imaging and biomarker data has considerable potential for discovering biomarkers and enhancing mechanistic understanding of AD and MCI.
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    Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net
    (Office of the Vice Chancellor for Research, 2012-04-13) Shen, Li; Kim, Sungeun; Qi, Yuan; Inlow, Mark; Swaminathan, Shanker; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Shaw, Leslie M.; Trojanowski, John Q.; Weiner, Michael W.; Saykin, Andrew J.; ADNI
    Abstract Multi-modal neuroimaging and biomarker data provide exciting opportunities to enhance our understanding of phenotypic characteristics associated with complex disorders. This study focuses on integrative analysis of structural MRI data and proteomic data from an RBM panel to examine their predictive power and identify relevant biomarkers in a large MCI/AD cohort. MRI data included volume and thickness measures of 98 regions estimated by FreeSurfer. RBM data included 146 proteomic analytes extracted from plasma and serum. A sparse learning model, elastic net logistic regression, was proposed to classify AD and MCI, and select disease-relevant biomarkers. A linear support vector machine coupled with feature selection was employed for comparison. Combining RBM and MRI data yielded improved prediction rates: HC vs AD (91.9%), HC vs MCI (90.5%) and MCI vs AD (86.5%). Elastic net identified a small set of meaningful imaging and proteomic biomarkers. The elastic net has great power to optimize the sparsity of feature selection while maintaining high predictive power. Its application to multi-modal imaging and biomarker data has considerable potential for discovering biomarkers and enhancing mechanistic understanding of AD and MCI.
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    Identifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease by correlation- and nonlinearity-aware sparse Bayesian learning
    (Institute of Electrical and Electronics Engineers, 2014-07) Wan, Jing; Zhang, Zhilin; Rao, Bhaskar D.; Fang, Shiaofen; Yan, Jingwen; Saykin, Andrew J.; Shen, Li; Department of Medicine, IU School of Medicine
    Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Traditionally, this task is performed by formulating a linear regression problem. Recently, it is found that using a linear sparse regression model can achieve better prediction accuracy. However, most existing studies only focus on the exploitation of sparsity of regression coefficients, ignoring useful structure information in regression coefficients. Also, these linear sparse models may not capture more complicated and possibly nonlinear relationships between cognitive performance and MRI measures. Motivated by these observations, in this work we build a sparse multivariate regression model for this task and propose an empirical sparse Bayesian learning algorithm. Different from existing sparse algorithms, the proposed algorithm models the response as a nonlinear function of the predictors by extending the predictor matrix with block structures. Further, it exploits not only inter-vector correlation among regression coefficient vectors, but also intra-block correlation in each regression coefficient vector. Experiments on the Alzheimer's Disease Neuroimaging Initiative database showed that the proposed algorithm not only achieved better prediction performance than state-of-the-art competitive methods, but also effectively identified biologically meaningful patterns.
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    Multimodal Neuroimaging Predictors for Cognitive Performance Using Structured Sparse Learning
    (Office of the Vice Chancellor for Research, 2013-04-05) Yan, Jingwen; Risacher, Shannon L.; Kim, Sungeun; Simon, Jacqueline C.; Li, Taiyong; Wan, Jing; Wang, Hua; Huang, Heng; Saykin, Andrew J.; Shen, Li
    Regression models have been widely studied to investigate whether multimodal neuroimaging measures can be used as effective biomarkers for predicting cognitive outcomes in the study of Alzheimer's Disease (AD). Most existing models overlook the interrelated structures either within neuroimaging measures or between cognitive outcomes, and thus may have limited power to yield optimal solutions. To address this issue, we propose to incorporate an L21 norm and/or a group L21 norm (G21 norm) in the regression models. Using ADNI-1 and ADNI-GO/2 data, we apply these models to examining the ability of structural MRI and AV-45 PET scans for predicting cognitive measures including ADAS and RAVLT scores. We focus our analyses on the participants with mild cognitive impairment (MCI), a prodromal stage of AD, in order to identify useful patterns for early detection. Compared with traditional linear and ridge regression methods, these new models not only demonstrate superior and more stable predictive performances, but also identify a small set of imaging markers that are biologically meaningful.