Interpreting null models of resting-state functional MRI dynamics: not throwing the model out with the hypothesis

Neuroimage. 2021 Nov:243:118518. doi: 10.1016/j.neuroimage.2021.118518. Epub 2021 Aug 29.


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.

Keywords: Autoregressive models; Fourier phase randomization; Null models; Surrogate data.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain / diagnostic imaging*
  • Brain Mapping / methods*
  • Computer Simulation
  • Data Interpretation, Statistical
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Magnetic Resonance Imaging / methods*
  • Models, Neurological
  • Models, Statistical
  • Rest / physiology*