Comparison of statistical tests for group differences in brain functional networks

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Brain functional connectivity has been studied by analyzing time series correlations in regional brain activities based on resting-state fMRI data. Brain functional connectivity can be depicted as a network or graph defined as a set of nodes linked by edges. Nodes represent brain regions and an edge measures the strength of functional correlation between two regions. Most of existing work focuses on estimation of such a network. A key but inadequately addressed question is how to test for possible differences of the networks between two subject groups, say between healthy controls and patients. Here we illustrate and compare the performance of several state-of-the-art statistical tests drawn from the neuroimaging, genetics, ecology and high-dimensional data literatures. Both real and simulated data were used to evaluate the methods. We found that Network Based Statistic (NBS) performed well in many but not all situations, and its performance critically depends on the choice of its threshold parameter, which is unknown and difficult to choose in practice. Importantly, two adaptive statistical tests called adaptive sum of powered score (aSPU) and its weighted version (aSPUw) are easy to use and complementary to NBS, being higher powered than NBS in some situations. The aSPU and aSPUw tests can also be applied to adjust for covariates. Between the aSPU and aSPUw tests, they often, but not always, performed similarly with neither one as a uniform winner. On the other hand, Multivariate Matrix Distance Regression (MDMR) has been applied to detect group differences for brain connectivity; with the usual choice of the Euclidean distance, MDMR is a special case of the aSPU test. Consequently NBS, aSPU and aSPUw tests are recommended to test for group differences in functional connectivity.

Original languageEnglish (US)
Pages (from-to)681-694
Number of pages14
StatePublished - Nov 1 2014



  • ASPU test
  • Brain network connectivity
  • High dimensional data
  • Neuroimaging
  • Rs-fMRI
  • Statistical power

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