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Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net

机译：通过弹性网识别MCI和AD的神经成像和蛋白质组生物标记

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摘要

Multi-modal neuroimaging and biomarker data provide exciting opportunities to enhance our understanding of phenotypic characteristics associated with complex disorders. This study focuses on inte-grative 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.
机译：多模式神经影像和生物标志物数据提供了令人兴奋的机会，可增强我们对与复杂疾病相关的表型特征的了解。这项研究的重点是对来自RBM小组的结构MRI数据和蛋白质组学数据进行综合分析，以检查它们的预测能力并确定大型MCI / AD队列中的相关生物标志物。 MRI数据包括FreeSurfer估算的98个区域的体积和厚度测量值。 RBM数据包括从血浆和血清中提取的146种蛋白质组学分析物。提出了一种稀疏的学习模型，即弹性网逻辑回归，对AD和MCI进行分类，并选择与疾病相关的生物标记。采用线性支持向量机与特征选择相结合进行比较。结合RBM和MRI数据可提高预测率：HC vs AD（91.9％），HC vs MCI（90.5％）和MCI vs AD（86.5％）。弹性网可以识别出少量有意义的成像和蛋白质组生物标志物。弹性网具有强大的功能，可以在保持高预测能力的同时优化特征选择的稀疏性。它在多模式成像和生物标志物数据中的应用在发现生物标志物和增强对AD和MCI的机理理解方面具有巨大潜力。

著录项

  • 来源
    《Multimodal Brain Image Analysis》|2011年|p.27-34|共8页
  • 会议地点 Toronto(CA);Toronto(CA);Toronto(CA);Toronto(CA)
  • 作者

    Li Shen; Sungeun Kim; Yuan Qi; Mark Inlow; Shanker Swaminathan; Kwangsik Nho; Jing Wan; Shannon L. Risacher; Leslie M. Shaw; John Q. Trojanowski; Michael W. Weiner; Andrew J. Saykin; ADNI;

  • 作者单位

    Radiology and Imaging Sciences, Indiana University, IN, USA;

    Radiology and Imaging Sciences, Indiana University, IN, USA;

    Computer Science, Statistics and Biology, Purdue University, IN, USA;

    Radiology and Imaging Sciences, Indiana University, IN, USA,Mathematics, Rose-Hulman Institute of Technology, IN, USA;

    Radiology and Imaging Sciences, Indiana University, IN, USA;

    Radiology and Imaging Sciences, Indiana University, IN, USA;

    Radiology and Imaging Sciences, Indiana University, IN, USA;

    Radiology and Imaging Sciences, Indiana University, IN, USA;

    Pathology and Laboratory Medicine, University of Pennsylvania, PA, USA;

    Pathology and Laboratory Medicine, University of Pennsylvania, PA, USA;

    Radiology, Medicine and Psychiatry, UC San Francisco, CA, USA;

    Radiology and Imaging Sciences, Indiana University, IN, USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 医用物理学;脑部疾病;
  • 关键词

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