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Accurate Identification of MCI Patients via Enriched White-Matter Connectivity Network

机译：通过丰富的白质连接网络准确识别MCI患者

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摘要

Mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD), is frequently considered to be a good target for early diagnosis and therapeutic interventions of AD. Recent emergence of reliable network characterization techniques have made understanding neurological disorders at a whole brain connectivity level possible. Accordingly, we propose a network-based multivariate classification algorithm, using a collection of measures derived from white-matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. An enriched description of WM connections, utilizing six physiological parameters, i.e., fiber penetration count, fractional anisotropy (FA), mean diffusivity (MD), and principal diffusivities (λ_1, λ_2, λ_3), results in six connectivity networks for each subject to account for the connection topology and the biophysical properties of the connections. Upon parcellating the brain into 90 regions-of-interest (ROIs), the average statistics of each ROI in relation to the remaining ROIs are extracted as features for classification. These features are then sieved to select the most discriminant subset of features for building an MCI classifier via support vector machines (SVMs). Cross-validation results indicate better diagnostic power of the proposed enriched WM connection description than simple description with any single physiological parameter.
机译：轻度认知障碍（MCI）通常是阿尔茨海默氏病（AD）的前驱期，通常被认为是AD早期诊断和治疗干预的良好靶标。可靠的网络表征技术的最新出现使得在全脑连接水平上了解神经系统疾病成为可能。因此，我们提出了一种基于网络的多元分类算法，它使用了从白质（WM）连接性网络中得出的一系列测量值，可以从正常对照中准确识别MCI患者。对WM连接的丰富描述，利用六个生理参数，即纤维渗透率，分数各向异性（FA），平均扩散率（MD）和主扩散率（λ_1，λ_2，λ_3），为每个受测者建立了六个连接网络考虑了连接拓扑和连接的生物物理特性。将大脑分成90个感兴趣区域（ROI）后，提取每个ROI与剩余ROI相关的平均统计数据作为分类特征。然后对这些特征进行筛选，以选择最有区别的特征子集，以通过支持向量机（SVM）构建MCI分类器。交叉验证结果表明，与具有任何单个生理参数的简单描述相比，所提出的丰富WM连接描述具有更好的诊断能力。

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  • 来源
    《Machine learning in medical imaging》|2010年|p.140-147|共8页
  • 会议地点 Beijing(CN);Beijing(CN);Beijing(CN);Beijing(CN)
  • 作者

    Chong-Yaw Wee; Pew-Thian Yap; Jeffery N. Browndyke; Guy G. Potter; David C. Steffens; Kathleen Welsh-Bohmer; Lihong Wang; Dinggang Shen;

  • 作者单位

    Department of Radiology, University of North Carolina at Chapel Hill, NC, U.S.A.;

    Department of Radiology, University of North Carolina at Chapel Hill, NC, U.S.A.;

    Joseph and Kathleen Bryan Alzheimer's Disease Research Center and;

    Department of Psychiatry and Behavioral Sciences;

    Department of Psychiatry and Behavioral Sciences;

    Joseph and Kathleen Bryan Alzheimer's Disease Research Center and;

    Brain Imaging and Analysis Center, Duke University Medical Center, U.S.A.;

    Department of Radiology, University of North Carolina at Chapel Hill, NC, U.S.A.;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 医用物理学;
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