Department of Biostatistical Sciences, Wake Forest School of Medicine Winston-Salem, NC, USA.
Front Neuroinform. 2011 Oct 14;5:22. doi: 10.3389/fninf.2011.00022. eCollection 2011.
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.
在这项工作中，我们使用了一种基于惩罚逻辑回归的大规模正则化方法，根据认知状态自动对结构磁共振成像（sMRI）进行分类。我们使用来自阿尔茨海默病神经影像学倡议（ADNI）临床数据库的 sMRI 数据说明了其性能。我们从 ADNI 网站下载了 98 名受试者（49 名认知正常和 49 名患者）的 sMRI 数据，这些数据是按年龄和性别匹配的。使用 SPM8 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 GLMNET 库实现的惩罚逻辑回归进行分类。为了避免乐观的估计分类准确性，灵敏度和特异性是基于数据的三向分裂和嵌套 10 倍交叉验证的组合来确定的。这种方法的一个主要特点是分类是基于大规模正则化的。当自动对认知正常受试者和阿尔茨海默病（AD）患者的 sMRI 图像进行分类时，本研究提出的方法具有很高的准确性、灵敏度和特异性。与白质体积图（分别为 81.1%、80.6%和 82.5%）相比，灰质（GM）体积图的准确性、灵敏度和特异性更高（分别为 85.7%、82.9%和 90%）。我们发现，使用 sMRI 脑数据，GM 和白质组织携带用于区分患者和认知正常受试者的有用信息。虽然我们已经证明了这种体素分类方法在区分认知正常受试者和 AD 患者方面的有效性，但原则上它可以应用于任何临床人群。
