Alzheimer's Disease (AD) and other neurodegenerative diseases affect over 20 million people worldwide, and this number is projected to significantly increase in the coming decades. Proposed imaging-based markers have shown steadily improving levels of sensitivity/specificity in classifying individual subjects as AD or normal. Several of these efforts have utilized statistical machine learning techniques, using brain images as input, as means of deriving such AD-related markers. A common characteristic of this line of research is a focus on either (1) using a single imaging modality for classification, or (2) incorporating several modalities, but reporting separate results for each. One strategy to improve on the success of these methods is to leverage all available imaging modalities together in a single automated learning framework. The rationale is that some subjects may show signs of pathology in one modality but not in another-by combining all available images a clearer view of the progression of disease pathology will emerge. Our method is based on the Multi-Kernel Learning (MKL) framework, which allows the inclusion of an arbitrary number of views of the data in a maximum margin, kernel learning framework. The principal innovation behind MKL is that it learns an optimal combination of kernel (similarity) matrices while simultaneously training a classifier. In classification experiments MKL outperformed an SVM trained on all available features by 3%-4%. We are especially interested in whether such markers are capable of identifying early signs of the disease. To address this question, we have examined whether our multi-modal disease marker (MMDM) can predict conversion from Mild Cognitive Impairment (MCI) to AD. Our experiments reveal that this measure shows significant group differences between MCI subjects who progressed to AD, and those who remained stable for 3 years. These differences were most significant in MMDMs based on imaging data. We also discuss the relationship between our MMDM and an individual's conversion from MCI to AD.

译文

阿尔茨海默氏病 (AD) 和其他神经退行性疾病影响全球超过2000万人,预计这一数字将在未来几十年中显着增加。提出的基于成像的标记物在将单个受试者分类为AD或正常受试者时显示出稳步提高的敏感性/特异性水平。这些工作中的一些已经利用统计机器学习技术,使用大脑图像作为输入,作为推导此类与AD相关的标记的手段。该研究领域的一个共同特征是专注于 (1) 使用单一的成像模式进行分类,或 (2) 合并几种模式,但每种模式都报告单独的结果。改善这些方法成功的一种策略是在单个自动学习框架中将所有可用的成像模式一起利用。理由是,某些受试者可能会在一种方式中显示出病理迹象,而在另一种方式中却没有-通过组合所有可用的图像,将会出现更清晰的疾病病理进展视图。我们的方法基于多核学习 (MKL) 框架,该框架允许在最大余量内核学习框架中包含任意数量的数据视图。MKL背后的主要创新在于,它在训练分类器的同时学习内核 (相似性) 矩阵的最佳组合。在分类实验中,通过3%-4%,MKL优于在所有可用特征上训练的SVM。我们特别感兴趣的是这些标记是否能够识别疾病的早期迹象。为了解决这个问题,我们研究了我们的多模式疾病标志物 (MMDM) 是否可以预测从轻度认知障碍 (MCI) 向AD的转化。我们的实验表明,该指标显示出进展为AD的MCI受试者与保持稳定3年的MCI受试者之间存在显着的群体差异。这些差异在基于成像数据的mmdm中最为显着。我们还讨论了MMDM与个人从MCI到AD的转换之间的关系。

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