High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization.
High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization.
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DOI：
10.3389/fninf.2011.00022
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发表时间：
2011
影响因子：
3.5
通讯作者：
Espeland MA
中科院分区：
文献类型：
--
作者：
Casanova R;Whitlow CT;Wagner B;Williamson J;Shumaker SA;Maldjian JA;Espeland MA
在这项工作中，我们使用一个大规模的正则化方法的基础上惩罚逻辑回归自动分类结构MRI图像（sMRI）根据认知状态。它的性能说明使用sMRI数据从阿尔茨海默病神经影像学倡议（ADNI）临床数据库。我们从ADNI网站上下载了98名受试者（49名认知正常者和49名患者）的sMRI数据，这些受试者的年龄和性别相匹配。使用SPM 8和ANTS软件包对图像进行分割和归一化。使用基于坐标下降优化技术的惩罚逻辑回归的GLMNET库实现进行分类。为了避免乐观估计，分类准确性、灵敏度和特异性基于数据的三向分割与嵌套10倍交叉验证的组合来确定。该方法的主要特点之一是基于大规模正则化进行分类。这里提出的方法是高度准确的，敏感的，和具体的自动分类时，认知正常的受试者和阿尔茨海默病（AD）患者的sMRI图像。与白色物质体积图（分别为81.1%、80.6%和82.5%）相比，灰质（GM）体积图（分别为85.7%、82.9%和90%）的准确性、灵敏度和特异性水平更高。我们发现，GM和白色物质组织携带有用的信息，用于使用sMRI大脑数据区分患者和认知正常受试者。虽然我们已经证明了这种逐体素分类方法在区分认知正常受试者和AD患者中的有效性，但原则上它可以应用于任何临床人群。
In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avoid optimistic estimates classification accuracy, sensitivity, and specificity were determined based on a combination of three-way split of the data with nested 10-fold cross-validations. One of the main features of this approach is that classification is performed based on large scale regularization. The methodology presented here was highly accurate, sensitive, and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease (AD) patients. Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter (GM) volume maps (85.7, 82.9, and 90%, respectively) compared to white matter volume maps (81.1, 80.6, and 82.5%, respectively). We found that GM and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data. Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from AD patients, in principle it could be applied to any clinical population.