Dynamic discrimination analysis: A spatial-temporal SVM
Dynamic discrimination analysis: A spatial-temporal SVM
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DOI：
10.1016/j.neuroimage.2007.02.020
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发表时间：
2007-05-15
期刊：
影响因子：
5.7
通讯作者：
Brammer, Michael
中科院分区：
文献类型：
--
作者：
Mouao-Miranda, Janaina;Friston, Karl J.;Brammer, Michael
最近，模式识别方法（如支持向量机（SVM））已被用于分析功能磁共振成像数据。在这些应用中，功能磁共振成像扫描被视为空间模式，并使用统计学习方法来识别区分大脑状态（例如，任务1与任务2）或受试者组（例如，患者和对照组）的数据的统计属性。我们建议使用时间嵌入对这些方法进行扩展。这使得fMRI时间序列的动态方面成为分类的明确部分。所提出的模式识别方法同时使用空间和时间信息。时间嵌入是通过定义fMRI的时空观测并对这些时间扩展观测应用支持向量机来实现的。这将产生一个包含体素和时间的区别性权重向量。所得到的向量在每个体素上提供有区别的响应，而不会对它们的时间形式施加任何限制。(C) 2007爱思唯尔公司版权所有。
Recently, pattern recognition methods (e.g., support vector machines (SVM)) have been used to analyze fMRI data. In these applications the fMRI scans are treated as spatial patterns and statistical learning methods are used to identify statistical properties of the data that discriminate between brain states (e.g., task 1 vs. task 2) or group of subjects (e.g., patients and controls). We propose an extension of these approaches using temporal embedding. This makes the dynamic aspect of fMRI time series an explicit part of the classification. The proposed pattern recognition approach uses both spatial and temporal information. Temporal embedding was implemented by defining spatiotemporal fMRI observations and applying a support vector machine to these temporally extended observations. This produces a discriminating weight vector encompassing both voxels and time. The resulting vector furnishes discriminating responses, at each voxel without imposing any constraints on their temporal form. (C) 2007 Elsevier Inc. All rights reserved.