Null models are useful for assessing whether a dataset exhibits a non-trivial property of interest. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fMRI) time series. Interpretation of null-model testing in this context may not be straightforward because (i) null hypotheses associated to different null models are sometimes unclear and (ii) fMRI metrics might be ‘trivial’, i.e. preserved under the null hypothesis, and still be useful in neuroimaging applications. In this commentary, we review several commonly used null models of fMRI time series and discuss the interpretation of the corresponding tests. We argue that, while null-model testing allows for a better characterization of the statistical properties of fMRI time series and associated metrics, it should not be considered as a mandatory validation step to assess their relevance in representing brain functional dynamics.

空模型对于评估数据集是否表现出重要的感兴趣属性很有用。这些模型最近引起了神经影像学界的兴趣，作为探索功能性磁共振成像 (fMRI) 时间序列动态特性的手段。在这种情况下解释零模型测试可能并不简单，因为 (i) 与不同零模型相关的零假设有时不清楚，并且 (ii) fMRI 指标可能是“微不足道的”，即保留在零假设下，并且仍然有用在神经影像学应用中。在这篇评论中，我们回顾了几个常用的 fMRI 时间序列空模型，并讨论了相应测试的解释。我们认为，