At conventional 1.5–3mm fMRI resolutions, with whole brain coverage, functional connectivity is often investigated by means of connectivity matrices, also described as functional connectomes. Here, we show how layer-dependent functional connectivity data can add additional dimensionalities and valuable directionality information to connectivity-matrix-analyses. Here, the Shen atlas (Shen et al. 2013) of 268 parcels was used to define approximate masks of brain areas. First, the parcels were transformed from MNI space to the individual participants EPI space with ANTs (Avants et al. 2008) using the non-linear warping SyN algorithm. Next, the time series of every layer was extracted individually for every brain area. Finally, Pearson correlation values of every time course with every other time course were estimated and depicted in connectivity matrix style.
