We propose a framework for feature extraction from learned low-dimensional subspaces that represent inter-subject variability. The manifold subspace is built from data-driven regions of interest (ROI). The regions are learned via sparse regression using the mini-mental state examination (MMSE) score as an independent variable which correlates better with the actual disease stage than a discrete class label. The sparse regression is used to perform variable selection along with a re-sampling scheme to reduce sampling bias. We then use the learned manifold coordinates to perform visualization and classification of the subjects. Results of the proposed approach are shown using the ADNI and ADNI-GO datasets. Three types of classification techniques, including a new MRI Disease-State-Score (MRI-DSS) classifier, are tested in conjunction with two learning strategies. In the first case Alzheimer's Disease (AD) and progressive mild cognitive impairment (pMCI) subjects were grouped together, while cognitive normal (CN) and stable mild cognitive impaired (sMCI) subjects were also grouped together. In the second approach, the classifiers are learned using the original class labels (with no grouping). We show results that are comparable to other state-of-the-art methods. A classification rate of 71%, of arguably the most clinically relevant subjects, sMCI and pMCI, is shown. Additionally, we present classification accuracies between CN and early MCI (eMCI) subjects, from the ADNI-GO dataset, of 65%. To our knowledge this is the first time classification accuracies for eMCI patients have been reported.

译文

我们提出了一个从学习的低维子空间中提取特征的框架,这些子空间表示主体间的可变性。流形子空间是从数据驱动的感兴趣区域 (ROI) 构建的。使用迷你精神状态检查 (MMSE) 得分作为自变量,通过稀疏回归来学习区域,该自变量与实际疾病阶段的相关性比离散类标签更好。稀疏回归用于执行变量选择以及重新抽样方案,以减少抽样偏差。然后,我们使用学习的流形坐标对主题进行可视化和分类。使用ADNI和ADNI-GO数据集显示了所提出方法的结果。结合两种学习策略测试了三种类型的分类技术,包括新的MRI疾病状态评分 (mri-dss) 分类器。在第一种情况下,阿尔茨海默氏病 (AD) 和进行性轻度认知障碍 (pMCI) 受试者被分组在一起,而认知正常 (CN) 和稳定的轻度认知障碍 (sMCI) 受试者也被分组在一起。在第二种方法中,使用原始类标签 (不分组) 学习分类器。我们展示的结果与其他最先进的方法相当。显示了可以说是最临床相关的受试者sMCI和pMCI的71% 的分类率。此外,我们从65% 的ADNI-GO数据集介绍了CN和早期MCI (eMCI) 受试者之间的分类准确性。据我们所知,这是首次报道eMCI患者的分类准确性。

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