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利用稀疏复合线性判别分析从多模态神经成像数据识别阿尔茨海默病相关脑区
Identifying Alzheimers Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis |
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| 课程网址: |
http://videolectures.net/nips2011_ye_alzheimers/
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| 主讲教师: |
Jieping Ye
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| 开课单位: |
密歇根大学
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| 开课时间: |
2012-09-06
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| 课程语种: |
英语
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| 中文简介: |
在疾病发展的早期诊断阿尔茨海默病（AD）具有重要的临床意义。目前的临床评估主要依赖于认知测量，其敏感性和特异性较低。快速发展的神经成像技术前景广阔。到目前为止，研究主要集中在单一的神经成像模式上。然而，由于不同的模式为相同的疾病病理提供了补充措施，多模式数据的融合可能会提高识别疾病相关大脑区域的统计能力。对于早期AD尤其如此，在该阶段，疾病相关区域最有可能是弱作用区域，很难单独从单一模式中检测到。我们提出了一种稀疏复合线性判别分析模型（SCLDA），用于从多模态数据中识别早期AD的疾病相关脑区。SCLDA使用一种新的公式，将每个LDA参数分解为所有模式共享的公共参数和每个模式特定的参数的乘积，从而能够对所有模式进行联合分析，并相互借鉴强度。我们证明了该公式等价于具有非凸正则化的惩罚似然，可通过DC（凸函数差分）规划求解。我们证明，在使用DC规划时，可以很好地揭示非凸正则化在保持弱效应特征方面的性质。我们进行了大量的仿真，以表明SCLDA在特征选择方面优于现有的竞争算法，特别是在识别弱效应特征的能力方面。我们将SCLDA应用于49例AD患者和67例正常对照（NC）的磁共振成像（MRI）和正电子发射断层扫描（PET）图像。我们的研究确定了与疾病相关的大脑区域，这与AD文献中的发现一致。
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| 课程简介: |
Diagnosis of Alzheimer's disease (AD) at the early stage of the disease development is of great clinical importance. Current clinical assessment that relies primarily on cognitive measures proves low sensitivity and specificity. The fast growing neuroimaging techniques hold great promise. Research so far has focused on single neuroimaging modalities. However, as different modalities provide complementary measures for the same disease pathology, fusion of multi-modality data may increase the statistical power in identification of disease-related brain regions. This is especially true for early AD, at which stage the disease-related regions are most likely to be weak-effect regions that are difficult to be detected from a single modality alone. We propose a sparse composite linear discriminant analysis model (SCLDA) for identification of disease-related brain regions of early AD from multi-modality data. SCLDA uses a novel formulation that decomposes each LDA parameter into a product of a common parameter shared by all the modalities and a parameter specific to each modality, which enables joint analysis of all the modalities and borrowing strength from one another. We prove that this formulation is equivalent to a penalized likelihood with non-convex regularization, which can be solved by the DC ((difference of convex functions) programming. We show that in using the DC programming, the property of the non-convex regularization in terms of preserving weak-effect features can be nicely revealed. We perform extensive simulations to show that SCLDA outperforms existing competing algorithms on feature selection, especially on the ability for identifying weak-effect features. We apply SCLDA to the Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images of 49 AD patients and 67 normal controls (NC). Our study identifies disease-related brain regions consistent with findings in the AD literature.
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| 关 键 词: |
阿尔茨海默病; 多模式数据的融合; 稀疏复合线性判别分析模型
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| 课程来源: |
视频讲座网
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| 数据采集: |
2021-12-25：zkj
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| 最后编审: |
2021-12-25：zkj
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| 阅读次数: |
52
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