Disease state prediction from resting state functional connectivity.
Disease state prediction from resting state functional connectivity.
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DOI：
10.1002/mrm.22159
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发表时间：
2009-12
影响因子：
3.3
通讯作者：
Mayberg, Helen S.
中科院分区：
文献类型：
--
作者：
Craddock, R. Cameron;Holtzheimer, Paul E., III;Hu, Xiaoping P.;Mayberg, Helen S.
关键词：
多体素模式分析方法的应用越来越受到人们的关注，特别是在脑状态预测和实时fMRI应用中。支持向量分类是这些技术中最受欢迎的，因为有报道称它具有更好的预测准确性并且对噪音不太敏感。应用支持向量分类来学习区分抑郁症患者和健康志愿者的功能连接模式。此外，实现了两种特征选择算法（一种过滤器方法，一种包装方法），将可靠性信息纳入特征选择过程。这些可靠性特征选择方法进行了比较，以前提出的两个特征选择方法。训练支持向量分类器，可靠地区分健康志愿者和临床抑郁症患者。可靠性特征选择方法优于以前使用的方法。提出的框架，应用支持向量分类功能连接数据是适用于其他疾病状态以外的抑郁症。
The application of multi-voxel pattern analysis methods has attracted increasing attention, particularly for brain state prediction and real-time fMRI 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.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
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DOI：
10.1098/rstb.2005.1634
发表时间：
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影响因子：
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影响因子：
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影响因子：
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