MRC Cognition and Brain Sciences Unit, Cambridge, UK.
Neuroimage. 2011 May 15;56(2):643-50. doi: 10.1016/j.neuroimage.2010.03.061. Epub 2010 Mar 27.
Multivariate pattern analysis is often assumed to rely on signals that directly reflect differences in the distribution of particular neural populations. The source of the signal used in these analyses remains unclear however, and an alternative model suggests that signal from larger draining veins may play a significant role. The current study was designed to investigate the vascular contribution to pattern analyses at 3T by comparing the results obtained from gradient and spin echo data. Classification analyses were carried out comparing line orientations in V1, tone frequencies in A1, and responses from different fingers in M1. In all cases, classification accuracy in the spin echo data was not significantly different from chance. In contrast, classification accuracies in the gradient echo data were significantly above chance, and significantly higher than the accuracies observed for the spin echo data. These results suggest that at the field strength and spatial resolution used for the majority of fMRI studies, a considerable proportion of the signal used by pattern analysis originates in the vasculature.
多变量模式分析通常假定依赖于直接反映特定神经元群体分布差异的信号。然而,这些分析中使用的信号源尚不清楚,另一种模型表明,来自较大引流静脉的信号可能发挥重要作用。本研究旨在通过比较梯度回波和自旋回波数据的结果,来研究 3T 下的血管对模式分析的贡献。分类分析比较了 V1 中的线方向、A1 中的音调频率以及 M1 中不同手指的反应。在所有情况下,自旋回波数据的分类准确率与随机水平无显著差异。相比之下,梯度回波数据的分类准确率明显高于随机水平,也明显高于自旋回波数据的准确率。这些结果表明,在大多数 fMRI 研究中使用的场强和空间分辨率下,模式分析所使用的信号中有相当大的一部分来源于脉管系统。