Grouped sparse Bayesian learning for voxel selection in multivoxel pattern analysis of fMRI data-猫眼课题宝

DOI：

10.1016/j.neuroimage.2018.09.031

发表时间：

2019-01

期刊：

NeuroImage

影响因子：

5.7

通讯作者：

中科院分区：

医学1区

文献类型：

作者：

Zhenfu Wen;Tianyou Yu;Z. Yu;Yuanqing Li

近年来，多体素模式分析（Multivoxel pattern analysis， MVPA）方法在功能磁共振成像（fMRI）数据分析中被广泛应用于人脑状态分类。体素选择在MVPA研究中起着重要的作用，不仅因为它可以提高解码精度，而且因为它有助于理解大脑的功能。在fMRI文献中提出了许多体素选择方法。然而，这些方法中的大多数要么忽略了fMRI数据的结构信息，要么需要额外的交叉验证程序来确定模型的超参数。在本文中，我们提出了一种用于二进制脑解码的体素选择方法，称为组稀疏贝叶斯逻辑回归（GSBLR）。该方法利用fMRI数据的组稀疏特性，采用分组自动相关性确定（GARD）作为模型参数的先验。GSBLR中的所有参数都可以自动估计，从而避免了额外的交叉验证。基于两个公开可用的fMRI数据集和模拟数据集的实验结果表明，GSBLR比几种最先进的方法具有更好的分类精度和更稳定的解决方案。

Multivoxel pattern analysis (MVPA) methods have been widely applied in recent years to classify human brain states in functional magnetic resonance imaging (fMRI) data analysis. Voxel selection plays an important role in MVPA studies not only because it can improve decoding accuracy but also because it is useful for understanding brain functions. There are many voxel selection methods that have been proposed in fMRI literature. However, most of these methods either overlook the structure information of fMRI data or require additional cross-validation procedures to determine the hyperparameters of the models. In the present work, we proposed a voxel selection method for binary brain decoding called group sparse Bayesian logistic regression (GSBLR). This method utilizes the group sparse property of fMRI data by using a grouped automatic relevance determination (GARD) as a prior for model parameters. All the parameters in the GSBLR can be estimated automatically, thereby avoiding additional cross-validation. Experimental results based on two publicly available fMRI datasets and simulated datasets demonstrate that GSBLR achieved better classification accuracies and yielded more stable solutions than several state-of-the-art methods.

Grouped sparse Bayesian learning for voxel selection in multivoxel pattern analysis of fMRI data

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