Thanks again Andy – I had another question (sorry!). Is the above approach what you would suggest for creating a normative connectome in common parcellations (i.e., loading each ROI of a parcellation separately as a seed and then using the ‘connectivity matrix’ option)?
While this works well in the smaller datasets, it seems to use a lot of memory in the NKI 169 atlas. For example, in a 512 parcellation of the cortex is uses over 128 GB of memory which is quite problematic. I thought perhaps there might be a more efficient option, or some extra pre-processing I could do to the dataset to reduce the memory load (although I could not find any documentation).
Thanks again for the help, I promise to stop bothering you soon!
