Due to the large number of time series, connectivity estimation between all pairs of time series in source-space is computationally demanding. To alleviate this problem, the user has the option to specify pairs of signals for which connectivity should be estimated, which makes it possible, for example, to compute the connectivity between a seed location and the rest of the brain. For all-to-all connectivity estimation in source-space, an attractive option is also to reduce the number of time series, and thus the computational demand, by summarizing the source time series within a set of cortical regions. We provide functions to do this automatically for cortical parcellations obtained by FreeSurfer, which employs probabilistic atlases and cortical folding patterns for an automated subject-specific segmentation of the cortex into anatomical regions (Fischl et al., 2004; Desikan et al., 2006; Destrieux et al., 2010). The obtained set of summary time series can then be used as input to the connectivity estimation. The association of time series with cortical regions simplifies the interpretation of results and it makes them directly comparable across subjects since, due
