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Effects of spatial smoothing on functional brain networks

机译：空间平滑对功能性大脑网络的影响

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Graph-theoretical methods have rapidly become a standard tool in studies of the structure and function of the human brain. Whereas the structural connectome can be fairly straightforwardly mapped onto a complex network, there are more degrees of freedom in constructing networks that represent functional connections between brain areas. For functional magnetic resonance imaging (fMRI) data, such networks are typically built by aggregating the blood-oxygen-level dependent signal time series of voxels into larger entities (such as Regions of Interest in some brain atlas) and determining their connection strengths from some measure of time-series correlations. Although it is evident that the outcome must be affected by how the voxel-level time series are treated at the preprocessing stage, there is a lack of systematic studies of the effects of preprocessing on network structure. Here, we focus on the effects of spatial smoothing, a standard preprocessing method for fMRI. We apply various levels of spatial smoothing to resting-state fMRI data and measure the changes induced in functional networks. We show that the level of spatial smoothing clearly affects the degrees and other centrality measures of functional network nodes; these changes are non-uniform, systematic, and depend on the geometry of the brain. The composition of the largest connected network component is also affected in a way that artificially increases the similarity of the networks of different subjects. Our conclusion is that wherever possible, spatial smoothing should be avoided when preprocessing fMRI data for network analysis. ?2017 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
机译：图 - 理论方法迅速成为人脑结构和功能研究的标准工具。虽然结构连接可以相当直接地映射到复杂的网络上，但在构建代表脑区域之间的功能连接的网络中有更多程度的自由度。对于功能磁共振成像（FMRI）数据，这种网络通常通过将血氧级依赖性信号时间序列系列序列（例如在某些脑图集地区的感兴趣区域）聚集并确定它们的连接强度时间序列相关性的测量。尽管很明显，结果必须受到体素级时间序列在预处理阶段进行处理的影响，但缺乏对预处理对网络结构的影响的系统研究。在这里，我们专注于空间平滑的影响，是FMRI的标准预处理方法。我们将各种水平的空间平滑施加到休息状态的FMRI数据，并测量功能网络中引起的变化。我们表明空间平滑水平清楚地影响了功能网络节点的程度和其他中心度量;这些变化是非均匀的，系统的，并且取决于大脑的几何形状。最大连接的网络组件的组成也在一种人为地增加不同受试者网络的相似性的方式影响。我们的结论是，在可能的情况下，应在预处理FMRI数据进行网络分析时避免空间平滑。 ？2017年作者。欧洲神经科学协会联合会发表的欧洲神经科学杂志和约翰瓦里＆儿子有限公司

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