Department of Instrument Science and Engineering, SEIEE, Shanghai Jiao Tong University, Dong Chuan Rd #800, Shanghai, China,
Neuroinformatics. 2014 Jul;12(3):381-94. doi: 10.1007/s12021-013-9218-x.
Neuroimaging provides a powerful tool to characterize neurodegenerative progression and therapeutic efficacy in Alzheimer's disease (AD) and its prodromal stage-mild cognitive impairment (MCI). However, since the disease pathology might cause different patterns of structural degeneration, which is not pre-known, it is still a challenging problem to identify the relevant imaging markers for facilitating disease interpretation and classification. Recently, sparse learning methods have been investigated in neuroimaging studies for selecting the relevant imaging biomarkers and have achieved very promising results on disease classification. However, in the standard sparse learning method, the spatial structure is often ignored, although it is important for identifying the informative biomarkers. In this paper, a sparse learning method with tree-structured regularization is proposed to capture patterns of pathological degeneration from fine to coarse scale, for helping identify the informative imaging biomarkers to guide the disease classification and interpretation. Specifically, we first develop a new tree construction method based on the hierarchical agglomerative clustering of voxel-wise imaging features in the whole brain, by taking into account their spatial adjacency, feature similarity and discriminability. In this way, the complexity of all possible multi-scale spatial configurations of imaging features can be reduced to a single tree of nested regions. Second, we impose the tree-structured regularization on the sparse learning to capture the imaging structures, and then use them for selecting the most relevant biomarkers. Finally, we train a support vector machine (SVM) classifier with the selected features to make the classification. We have evaluated our proposed method by using the baseline MR images of 830 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, which includes 198 AD patients, 167 progressive MCI (pMCI), 236 stable MCI (sMCI), and 229 normal controls (NC). Our experimental results show that our method can achieve accuracies of 90.2 %, 87.2 %, and 70.7 % for classifications of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, demonstrating promising performance compared with other state-of-the-art methods.
神经影像学为阿尔茨海默病（AD）及其前驱期——轻度认知障碍（MCI）的神经退行性进展和治疗效果提供了一种强大的工具。然而，由于疾病病理可能导致不同模式的结构退化，而这些退化模式是未知的，因此，识别相关的影像学标志物以促进疾病的解释和分类仍然是一个具有挑战性的问题。最近，稀疏学习方法已在神经影像学研究中得到了研究，用于选择相关的影像学生物标志物，并在疾病分类方面取得了非常有希望的结果。然而，在标准的稀疏学习方法中，空间结构通常被忽略，尽管它对于识别信息丰富的生物标志物很重要。在本文中，提出了一种具有树结构正则化的稀疏学习方法，用于从精细到粗糙的尺度上捕获病理退化模式，以帮助识别信息丰富的影像学生物标志物，从而指导疾病分类和解释。具体来说，我们首先开发了一种新的树构建方法，该方法基于全脑影像特征的层次聚类，同时考虑了它们的空间邻接、特征相似性和可区分性。通过这种方式，可以将所有可能的多尺度空间配置的影像特征的复杂性降低到单个嵌套区域的树中。其次，我们将树结构正则化应用于稀疏学习中，以捕获影像结构，然后使用它们来选择最相关的生物标志物。最后，我们使用选择的特征训练支持向量机（SVM）分类器进行分类。我们使用来自阿尔茨海默病神经影像学倡议（ADNI）数据库的 830 名受试者的基线磁共振图像对我们提出的方法进行了评估，其中包括 198 名 AD 患者、167 名进展性 MCI（pMCI）、236 名稳定性 MCI（sMCI）和 229 名正常对照（NC）。我们的实验结果表明，对于 AD 与 NC、pMCI 与 NC 以及 pMCI 与 sMCI 的分类，我们的方法分别可以达到 90.2%、87.2%和 70.7%的准确率，与其他最先进的方法相比，表现出了有希望的性能。
