Chen Sea, Bouman Charles A, Lowe Mark J
Division of Imaging Sciences, Department of Radiology, Indiana University, School of Medicine, Indianapolis, IN, USA.
IEEE Trans Med Imaging. 2004 Jan;23(1):85-98. doi: 10.1109/TMI.2003.819922.
A common method of increasing hemodynamic response (SNR) in functional magnetic resonance imaging (fMRI) is to average signal timecourses across voxels. This technique is potentially problematic because the hemodynamic response may vary across the brain. Such averaging may destroy significant features in the temporal evolution of the fMRI response that stem from either differences in vascular coupling to neural tissue or actual differences in the neural response between two averaged voxels. Two novel techniques are presented in this paper in order to aid in an improved SNR estimate of the hemodynamic response while preserving statistically significant voxel-wise differences. The first technique is signal subspace estimation for periodic stimulus paradigms that involves a simple thresholding method. This increases SNR via dimensionality reduction. The second technique that we call clustered components analysis is a novel amplitude-independent clustering method based upon an explicit statistical data model. It includes an unsupervised method for estimating the number of clusters. Our methods are applied to simulated data for verification and comparison to other techniques. A human experiment was also designed to stimulate different functional cortices. Our methods separated hemodynamic response signals into clusters that tended to be classified according to tissue characteristics.
在功能磁共振成像（fMRI）中增加血流动力学响应（信噪比）的一种常见方法是对体素间的信号时间历程进行平均。这种技术可能存在问题，因为血流动力学响应在大脑中可能会有所不同。这种平均可能会破坏fMRI响应时间演变中的显著特征，这些特征要么源于血管与神经组织耦合的差异，要么源于两个平均体素之间神经反应的实际差异。本文提出了两种新技术，以帮助在保留具有统计学意义的体素级差异的同时，改进血流动力学响应的信噪比估计。第一种技术是针对周期性刺激范式的信号子空间估计，它涉及一种简单的阈值方法。这通过降维来提高信噪比。我们称为聚类成分分析的第二种技术是一种基于显式统计数据模型的新型与幅度无关的聚类方法。它包括一种用于估计聚类数量的无监督方法。我们的方法应用于模拟数据以进行验证并与其他技术进行比较。还设计了一项人体实验来刺激不同的功能皮层。我们的方法将血流动力学响应信号分离为聚类，这些聚类倾向于根据组织特征进行分类。
