Browsing by Author "Tan, Lan"

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    Identifying Candidate Genetic Associations with MRI-Derived AD-Related ROI via Tree-Guided Sparse Learning
    (IEEE, 2019-11) Hao, Xiaoke; Yao, Xiaohui; Risacher, Shannon L.; Saykin, Andrew J.; Yu, Jintai; Wang, Huifu; Tan, Lan; Shen, Li; Zhang, Daoqiang; Radiology and Imaging Sciences, School of Medicine
    Imaging genetics has attracted significant interests in recent studies. Traditional work has focused on mass-univariate statistical approaches that identify important single nucleotide polymorphisms (SNPs) associated with quantitative traits (QTs) of brain structure or function. More recently, to address the problem of multiple comparison and weak detection, multivariate analysis methods such as the least absolute shrinkage and selection operator (Lasso) are often used to select the most relevant SNPs associated with QTs. However, one problem of Lasso, as well as many other feature selection methods for imaging genetics, is that some useful prior information, e.g., the hierarchical structure among SNPs, are rarely used for designing a more powerful model. In this paper, we propose to identify the associations between candidate genetic features (i.e., SNPs) and magnetic resonance imaging (MRI)-derived measures using a tree-guided sparse learning (TGSL) method. The advantage of our method is that it explicitly models the complex hierarchical structure among the SNPs in the objective function for feature selection. Specifically, motivated by the biological knowledge, the hierarchical structures involving gene groups and linkage disequilibrium (LD) blocks as well as individual SNPs are imposed as a tree-guided regularization term in our TGSL model. Experimental studies on simulation data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) data show that our method not only achieves better predictions than competing methods on the MRI-derived measures of AD-related region of interests (ROIs) (i.e., hippocampus, parahippocampal gyrus, and precuneus), but also identifies sparse SNP patterns at the block level to better guide the biological interpretation.
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    Plasma phosphorylated-tau181 as a predictive biomarker for Alzheimer's amyloid, tau and FDG PET status
    (Springer Nature, 2021-11-13) Shen, Xue-Ning; Huang, Yu-Yuan; Chen, Shi-Dong; Guo, Yu; Tan, Lan; Dong, Qiang; Yu, Jin-Tai; Alzheimer’s Disease Neuroimaging Initiative; Neurology, School of Medicine
    Plasma phosphorylated-tau181 (p-tau181) showed the potential for Alzheimer's diagnosis and prognosis, but its role in detecting cerebral pathologies is unclear. We aimed to evaluate whether it could serve as a marker for Alzheimer's pathology in the brain. A total of 1189 participants with plasma p-tau181 and PET data of amyloid, tau or FDG PET were included from ADNI. Cross-sectional relationships of plasma p-tau181 with PET biomarkers were tested. Longitudinally, we further investigated whether different p-tau181 levels at baseline predicted different progression of Alzheimer's pathological changes in the brain. We found plasma p-tau181 significantly correlated with brain amyloid (Spearman ρ = 0.45, P < 0.0001), tau (0.25, P = 0.0003), and FDG PET uptakes (-0.37, P < 0.0001), and increased along the Alzheimer's continuum. Individually, plasma p-tau181 could detect abnormal amyloid, tau pathologies and hypometabolism in the brain, similar with or even better than clinical indicators. The diagnostic accuracy of plasma p-tau181 elevated significantly when combined with clinical information (AUC = 0.814 for amyloid PET, 0.773 for tau PET, and 0.708 for FDG PET). Relationships of plasma p-tau181 with brain pathologies were partly or entirely mediated by the corresponding CSF biomarkers. Besides, individuals with abnormal plasma p-tau181 level (>18.85 pg/ml) at baseline had a higher risk of pathological progression in brain amyloid (HR: 2.32, 95%CI 1.32-4.08) and FDG PET (3.21, 95%CI 2.06-5.01) status. Plasma p-tau181 may be a sensitive screening test for detecting brain pathologies, and serve as a predictive biomarker for Alzheimer's pathophysiology.
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    Staging tau pathology with tau PET in Alzheimer’s disease: a longitudinal study
    (Springer, 2021) Chen, Shi-Dong; Lu, Jia-Ying; Li, Hong-Qi; Yang, Yu-Xiang; Jiang, Jie-Hui; Cui, Mei; Zuo, Chuan-Tao; Tan, Lan; Dong, Qiang; Yu, Jin-Tai; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of Medicine
    A biological research framework to define Alzheimer’ disease with dichotomized biomarker measurement was proposed by National Institute on Aging–Alzheimer’s Association (NIA–AA). However, it cannot characterize the hierarchy spreading pattern of tau pathology. To reflect in vivo tau progression using biomarker, we constructed a refined topographic 18F-AV-1451 tau PET staging scheme with longitudinal clinical validation. Seven hundred and thirty-four participants with baseline 18F-AV-1451 tau PET (baseline age 73.9 ± 7.7 years, 375 female) were stratified into five stages by a topographic PET staging scheme. Cognitive trajectories and clinical progression were compared across stages with or without further dichotomy of amyloid status, using linear mixed-effect models and Cox proportional hazard models. Significant cognitive decline was first observed in stage 1 when tau levels only increased in transentorhinal regions. Rates of cognitive decline and clinical progression accelerated from stage 2 to stage 3 and stage 4. Higher stages were also associated with greater CSF phosphorylated tau and total tau concentrations from stage 1. Abnormal tau accumulation did not appear with normal β-amyloid in neocortical regions but prompt cognitive decline by interacting with β-amyloid in temporal regions. Highly accumulated tau in temporal regions independently led to cognitive deterioration. Topographic PET staging scheme have potentials in early diagnosis, predicting disease progression, and studying disease mechanism. Characteristic tau spreading pattern in Alzheimer’s disease could be illustrated with biomarker measurement under NIA–AA framework. Clinical–neuroimaging–neuropathological studies in other cohorts are needed to validate these findings.