Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data
Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data
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DOI：
10.1016/j.neuroimage.2005.06.070
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发表时间：
2005-12-01
期刊：
影响因子：
5.7
通讯作者：
Stetter, M
中科院分区：
文献类型：
--
作者：
Mourao-Miranda, J;Bokde, ALW;Stetter, M
在本研究中，我们采用支持向量机（SVM）算法进行多变量分类的大脑状态从整个功能磁共振成像（fMRI）的体积没有事先选择的空间特征。此外，我们做了SVM和Fisher线性判别（FLD）分类器之间的比较分析。我们将该方法应用于两个多受试者注意力实验：人脸匹配和位置匹配任务。我们证明，SVM优于FLD的分类性能以及在鲁棒性的空间地图（即区分卷）。此外，与FLD相比，SVM判别图与一般线性模型（GLM）分析有更大的重叠。分析分为两个阶段：在训练过程中，分类器算法找到一组区域，通过这些区域可以最好地区分两种大脑状态。在下一阶段，测试阶段，给定来自新受试者的fMRI体积，分类器预测受试者的瞬时大脑状态。(c)2005年爱思唯尔公司All rights reserved.
In the present study, we applied the Support Vector Machine (SVM) algorithm to perform multivariate classification of brain states from whole functional magnetic resonance imaging (fMRI) volumes without prior selection of spatial features. In addition, we did a comparative analysis between the SVM and the Fisher Linear Discriminant (FLD) classifier. We applied the methods to two multisubject attention experiments: a face matching and a location matching task. We demonstrate that SVM outperforms FLD in classification performance as well as in robustness of the spatial maps obtained (i.e. discriminating volumes). In addition, the SVM discrimination maps had greater overlap with the general linear model (GLM) analysis compared to the FLD. The analysis presents two phases: during the training, the classifier algorithm finds the set of regions by which the two brain states can be best distinguished from each other. In the next phase, the test phase, given an fMRI volume from a new subject, the classifier predicts the subject's instantaneous brain state. (c) 2005 Elsevier Inc. All rights reserved.