School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
Magn Reson Med. 2009 Dec;62(6):1619-28. doi: 10.1002/mrm.22159.
The application of multivoxel pattern analysis methods has attracted increasing attention, particularly for brain state prediction and real-time functional MRI applications. Support vector classification is the most popular of these techniques, owing to reports that it has better prediction accuracy and is less sensitive to noise. Support vector classification was applied to learn functional connectivity patterns that distinguish patients with depression from healthy volunteers. In addition, two feature selection algorithms were implemented (one filter method, one wrapper method) that incorporate reliability information into the feature selection process. These reliability feature selections methods were compared to two previously proposed feature selection methods. A support vector classifier was trained that reliably distinguishes healthy volunteers from clinically depressed patients. The reliability feature selection methods outperformed previously utilized methods. The proposed framework for applying support vector classification to functional connectivity data is applicable to other disease states beyond major depression.
多体素模式分析方法的应用引起了越来越多的关注，特别是在脑状态预测和实时功能磁共振成像应用中。支持向量分类是这些技术中最受欢迎的方法，因为有报道称它具有更高的预测准确性，并且对噪声的敏感性较低。支持向量分类被应用于学习功能连接模式，以区分抑郁症患者和健康志愿者。此外，还实现了两种特征选择算法（一种过滤方法，一种封装方法），将可靠性信息纳入特征选择过程。将这些可靠性特征选择方法与之前提出的两种特征选择方法进行了比较。训练了一个支持向量分类器，可以可靠地区分健康志愿者和临床抑郁患者。可靠性特征选择方法优于之前使用的方法。所提出的将支持向量分类应用于功能连接数据的框架适用于除重度抑郁症以外的其他疾病状态。
