Most approaches to the estimation of brain functional connectivity from the functional magnetic resonance imaging (fMRI) data rely on computing some measure of statistical dependence, or more generally, a distance between univariate representative time series of regions of interest (ROIs) consisting of multiple voxels. However, summarizing a ROI's multiple time series with its mean or the first principal component (1PC) may result to the loss of information as, for example, 1PC explains only a small fraction of variance of the multivariate signal of the neuronal activity. We propose to compare ROIs directly, without the use of representative time series, defining a new measure of multivariate connectivity between ROIs, not necessarily consisting of the same number of voxels, based on the Wasserstein distance. We assess the proposed Wasserstein functional connectivity measure on the autism screening task, demonstrating its superiority over commonly used univariate and multivariate functional connectivity measures.
翻译:从功能磁共振成像(fMRI)数据估算大脑功能连接的方法大多依赖于计算某种统计依赖度,或更笼统地说,由多种氧化物组成的相关区域独一代有代表性的时间序列之间的距离。然而,将ROI的多个时间序列及其平均值或第一个主要组成部分(PC)加以总结,可能导致信息丢失,例如,1PC只解释了神经活动多变量信号差异的一小部分。我们提议在不使用具有代表性的时间序列的情况下,直接比较ROI,以确定ROI之间多变量连接的新尺度,不一定包括基于瓦瑟斯坦距离的相同数量的 voxel。我们评估了拟议的关于自闭症筛查任务的瓦瑟斯坦功能连接措施,表明它优于常用的单向和多变量功能连接措施。