The use of nonparametric permutation-based statistical tests for the analysis of functional
neuroimaging data (mainly EEG, MEG, and fMRI) is a common approach for comparing observations
under different conditions, or in different subject groups. There are variations of
these methods to analyze brain signals obtained along the spatial and temporal dimensions,
and also across frequency, which accounts for oscillatory activity. However, there
is no well-known method for nonparametric testing of the measured functional connectivity
between different areas of the brain. In this paper, we introduce a modified version
of an existing nonparametric testing framework, which we adapted for use with functional
connectivity data. By using this method, we will show that there are brain areas between
which patients with Alzheimer’s disease have reduced functional connectivity in an
eyes-closed resting state, based on EEG data.
