论文标题
自闭症筛查的多元WASSERSERSTEIN功能连接
Multivariate Wasserstein Functional Connectivity for Autism Screening
论文作者
论文摘要
从功能磁共振成像（fMRI）数据中估算大脑功能连通性的大多数方法都依赖于计算统计依赖性的某些度量，或者更一般地，单变量代表性的时间序列的利益区域（ROI）（ROI）（ROI）组成的距离。但是，总结ROI的多个时间序列具有其平均值或第一个主成分（1pc）可能导致信息丢失，例如，1PC仅解释了神经元活动的多变量信号的一小部分。 我们建议在不使用代表性时间序列的情况下直接比较ROI，从而根据Wasserstein距离定义了ROI之间的新的多元连通性量度，不一定由相同数量的Voxels组成。我们在自闭症筛查任务上评估了拟议中的Wasserstein功能连接度量，证明了其优于常用单变量和多元功能连通性测量指标。
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.
