Division of Anaesthesia, University of Cambridge, Cambridge, UK.
Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Nat Commun. 2024 Jun 4;15(1):4745. doi: 10.1038/s41467-024-48781-5.
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
大脑区域之间的功能相互作用可以看作是一个网络，使神经科学家能够通过网络科学研究大脑功能。在这里，我们系统地评估了 768 种从静息态功能磁共振成像中重建网络的数据分析处理流程，评估了脑区划分、连接定义和全局信号回归的影响。我们的标准旨在寻找最小化运动混淆和网络拓扑虚假测试-重测差异的数据分析处理流程，同时对个体间差异和感兴趣的实验效应保持敏感。我们揭示了不同数据分析处理流程在功能连接组学方面的适用性存在巨大且系统的差异。数据分析处理流程的不当选择不仅会产生误导性结果，而且会产生系统偏差，大多数数据分析处理流程至少有一个标准不满足。然而，一组最优的数据分析处理流程在不同的数据集上始终满足所有标准，涵盖了几分钟、几周和几个月的时间跨度。我们提供了每个数据分析处理流程在不同标准和数据集上的性能的详细信息，以告知未来功能连接组学的最佳实践。
