A sparse representation-based algorithm for pattern localization in brain imaging data analysis.
A sparse representation-based algorithm for pattern localization in brain imaging data analysis.
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一种基于稀疏表示的脑成像数据分析模式定位算法
DOI：
10.1371/journal.pone.0050332
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Sun P
中科院分区：
文献类型：
--
作者：
Li Y;Long J;He L;Lu H;Gu Z;Sun P
针对脑成像数据分析中的两类分类问题，提出了一种基于稀疏表示的多变量模式分析（MVPA）算法，分别定位不同刺激类别/脑状态下的脑激活模式。特征选择可以建模为稀疏表示（或稀疏回归）问题。该技术已成功地应用于fMRI数据分析中的体素选择。然而，基于稀疏表示或其他方法的单一选择很容易获得信息量最大的特征的子集，而不是全部。在这里，我们提出的算法递归地消除稀疏回归方法选择的信息特征，直到基于剩余特征的解码精度下降到接近机会水平的阈值。以这种方式，包括所有识别的特征的结果特征集被期望涉及用于区分的所有信息特征。根据稀疏回归权重的符号，这些选择的特征被分成对应于两个刺激类/大脑状态的两个集合。接下来，为了去除两个选择的特征集中的不相关/噪声特征，我们在个体受试者水平或组水平上执行非参数排列测试。在数据分析中，我们用玩具数据集和固有信号光学成像数据集验证了我们的算法。实验结果表明，该算法能够准确地定位两个类相关模式。作为一个应用程序的例子，我们使用我们的算法上的功能磁共振成像（fMRI）数据集。两组信息体素，对应于两个语义类别（即，“老年人”和“年轻人”）。
Considering the two-class classification problem in brain imaging data analysis, we propose a sparse representation-based multi-variate pattern analysis (MVPA) algorithm to localize brain activation patterns corresponding to different stimulus classes/brain states respectively. Feature selection can be modeled as a sparse representation (or sparse regression) problem. Such technique has been successfully applied to voxel selection in fMRI data analysis. However, single selection based on sparse representation or other methods is prone to obtain a subset of the most informative features rather than all. Herein, our proposed algorithm recursively eliminates informative features selected by a sparse regression method until the decoding accuracy based on the remaining features drops to a threshold close to chance level. In this way, the resultant feature set including all the identified features is expected to involve all the informative features for discrimination. According to the signs of the sparse regression weights, these selected features are separated into two sets corresponding to two stimulus classes/brain states. Next, in order to remove irrelevant/noisy features in the two selected feature sets, we perform a nonparametric permutation test at the individual subject level or the group level. In data analysis, we verified our algorithm with a toy data set and an intrinsic signal optical imaging data set. The results show that our algorithm has accurately localized two class-related patterns. As an application example, we used our algorithm on a functional magnetic resonance imaging (fMRI) data set. Two sets of informative voxels, corresponding to two semantic categories (i.e., “old people” and “young people”), respectively, are obtained in the human brain.
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影响因子：
2.9
作者：
Hanson, Stephen Jose;Halchenko, Yaroslav O.
通讯作者：
Halchenko, Yaroslav O.
DOI：
10.1073/pnas.88.5.1621
发表时间：
1991-03-01
影响因子：
11.1
作者：
HAXBY, JV;GRADY, CL;RAPOPORT, SI
通讯作者：
RAPOPORT, SI
影响因子：
5.7
作者：
LaConte, S;Strother, S;Hu, XP
通讯作者：
Hu, XP
影响因子：
25
作者：
Kamitani, Y;Tong, F
通讯作者：
Tong, F
影响因子：
2
作者：
Goebel, Rainer;van Atteveldt, Nienke
通讯作者：
van Atteveldt, Nienke