Song Xiaomu, Chen Nan-kuei
Department of Electrical Engineering, School of Engineering, Widener University, Kirkbride Hall, Room 369, One University Place, Chester, PA 19013, USA.
Brain Imaging and Analysis Center, Duke University Medical Center, Box 2737, Hock Plaza, Durham, NC 27710, USA.
Magn Reson Imaging. 2014 Sep;32(7):819-31. doi: 10.1016/j.mri.2014.04.004. Epub 2014 Apr 13.
Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies.
静息态功能磁共振成像（fMRI）旨在测量独立于特定功能任务的基线神经元连接性，并捕捉由于神经系统疾病导致的连接性变化。大多数现有的网络检测方法依赖于固定阈值来识别静息状态下功能连接的体素。由于fMRI的非平稳性，该阈值无法适应不同会话和受试者之间数据特征的变化，从而产生不可靠的映射结果。在本研究中，提出了一种用于静息态fMRI数据分析的新方法。具体而言，将静息态网络映射公式化为一个异常值检测过程，该过程使用一类支持向量机（SVM）来实现。通过使用空间特征域原型选择方法和二类SVM重新分类对结果进行细化。对每个体素的最终决策是通过比较其功能连接和未连接的概率而不是阈值来做出的。使用基于SVM的特征选择方法提取并检查了多个用于静息态分析的特征，并确定了最具代表性的特征。使用合成和实验性fMRI数据对所提出的方法进行了评估。还与独立成分分析（ICA）和相关性分析进行了比较研究。实验结果表明，所提出的方法可以提供与ICA和相关性分析相当或更好的网络检测性能。该方法有可能应用于各种静息态定量fMRI研究。
