Generative modeling of brain maps with spatial autocorrelation

Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates…

• SF
  Simons Foundation Autism Research Initiative
• NI
  National Institutes of Health

  Awards: U01MH121766, P50AA012870, R01MH112746, R01MH112189, R01MH108590