Functional magnetic resonance imaging (fMRI) functional connectivity between brain regions is often computed using parcellations defined by functional or structural atlases. Typically, some kind of voxel averaging is performed to obtain a single temporal correlation estimate per region pair. However, several estimators can be defined for this task, with various assumptions and degrees of robustness to local noise, global noise, and region size. In this paper, we systematically present and study the properties of 9 different functional connectivity estimators taking into account the spatial structure of fMRI data, based on a simple fMRI data spatial model. These include 3 existing estimators and 6 novel estimators. We demonstrate the empirical properties of the estimators using synthetic, animal, and human data, in terms of graph structure, repeatability and reproducibility, discriminability, dependence on region size, as well as local and global noise robustness. We prove analytically the link between regional intra-correlation and inter-region correlation, and show that the choice of estimator has a strong influence on inter-correlation values.

翻译：功能磁共振成像（fMRI）中脑区间的功能连接通常通过基于功能或结构图谱的划分进行计算。典型做法是采用某种体素平均方法，为每对区域获取单一的时间相关性估计。然而，针对该任务可定义多种估计器，它们对局部噪声、全局噪声及区域大小具有不同的假设与鲁棒性。本文基于一个简单的fMRI数据空间模型，系统性地提出并研究了9种考虑fMRI数据空间结构的功能连接估计器的特性，其中包括3种现有估计器和6种新估计器。我们利用合成数据、动物数据及人类数据，从图结构、可重复性与再现性、可区分性、对区域大小的依赖性，以及局部和全局噪声鲁棒性等方面，实证展示了这些估计器的特性。同时，我们通过理论分析证明了区域内相关性与脑区间相关性之间的联系，并表明估计器的选择对相关性值具有显著影响。