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：大脑区域之间的功能磁共振成像(fMRI)功能连接常常使用功能或结构地图册界定的包状图来计算。一般情况下,为了获得每个区域对一对的单一时间相关估计,进行某种 voxel 平均法,但是,可以为此任务确定若干估计器,对当地噪音、全球噪音和区域大小有不同的假设和强度。在本文件中,我们系统地提出并研究9个不同功能连接估计器的特性,其中考虑到FMRI数据的空间结构,以简单的FMRI数据空间模型为基础。其中包括3个现有的估计器和6个新的估计器。我们用合成、动物和人类数据,从图表结构、可重复性和可复制性、可区别性、对区域大小的依赖性以及地方和全球噪音稳健性等方面,展示了估计器的经验特性。我们通过分析证明区域内部关系和区域间关联之间的联系,并表明估计器的选择对区域间关系价值有强烈的影响。</s>