[ljh]

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!

[ljh]

Hi Andy,
Thank you for the reply – you are indeed correct! The output looks fine.

As a follow up question – I want to investigate the structural connectivity between a seed roi (a lesion) and a common parcellation (rather than at the voxel level). To do this I have entered a the lesion as a seed roi amongst 246 other parcellation seeds and selected the ‘connectivity matrix’ option. This produces a 247 x 247 matrix which I assume denotes the number of streamlines between each of the regions I have entered into the seeds (246 parcellation rois and one lesion) – is that correct? (and if so – why does the diagonal contain values?)

Thanks again for the help – lead dbs is a fantastic tool!

Best,
Luke

[Author]

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