Andellini Martina, Cannatà Vittorio, Gazzellini Simone, Bernardi Bruno, Napolitano Antonio
Medical Physics Department, Enterprise Risk Management, Bambino Gesù Children's Hospital, Rome, Lazio, Italy.
Medical Physics Department, Enterprise Risk Management, Bambino Gesù Children's Hospital, Rome, Lazio, Italy.
J Neurosci Methods. 2015 Sep 30;253:183-92. doi: 10.1016/j.jneumeth.2015.05.020. Epub 2015 Jun 11.
The employment of graph theory to analyze spontaneous fluctuations in resting state BOLD fMRI data has become a dominant theme in brain imaging studies and neuroscience. Analysis of resting state functional brain networks based on graph theory has proven to be a powerful tool to quantitatively characterize functional architecture of the brain and it has provided a new platform to explore the overall structure of local and global functional connectivity in the brain. Due to its increased use and possible expansion to clinical use, it is essential that the reliability of such a technique is very strongly assessed. In this review, we explore the outcome of recent studies in network reliability which apply graph theory to analyze connectome resting state networks. Therefore, we investigate which preprocessing steps may affect reproducibility the most. In order to investigate network reliability, we compared the test-retest (TRT) reliability of functional data of published neuroimaging studies with different preprocessing steps. In particular we tested influence of global signal regression, correlation metric choice, binary versus weighted link definition, frequency band selection and length of time-series. Statistical analysis shows that only frequency band selection and length of time-series seem to affect TRT reliability. Our results highlight the importance of the choice of the preprocessing steps to achieve more reproducible measurements.
运用图论分析静息态血氧水平依赖性功能磁共振成像（BOLD fMRI）数据中的自发波动，已成为脑成像研究和神经科学中的一个主导主题。基于图论分析静息态功能脑网络，已被证明是定量表征大脑功能结构的有力工具，并且为探索大脑局部和全局功能连接的整体结构提供了一个新平台。鉴于其使用频率增加以及可能扩展至临床应用，对这种技术的可靠性进行非常严格的评估至关重要。在这篇综述中，我们探讨了近期网络可靠性研究的成果，这些研究应用图论分析连接组静息态网络。因此，我们研究了哪些预处理步骤可能对可重复性影响最大。为了研究网络可靠性，我们比较了已发表的神经成像研究中不同预处理步骤下功能数据的重测（TRT）可靠性。特别是，我们测试了全局信号回归、相关度量选择、二元与加权链接定义、频带选择以及时间序列长度的影响。统计分析表明，似乎只有频带选择和时间序列长度会影响TRT可靠性。我们的结果凸显了选择预处理步骤以实现更具可重复性测量的重要性。
