I am using the nilearn function nilearn.datasets.fetch_coords_seitzman_2018() and wondering if the dictionary structure provided is the correct one? It seems like the region labels are not specific enough to denote the region within structure. For instance, inspecting .regions returns ‘basalGanglia’ for several regions rather what their specific label within a region.
If anyone could help point me in the right direction here that would be much appreciated!
Along these lines, if I wanted to extract 3 regions from one atlas and 4 regions from another and put them in a single 3D volume (with each region depicted as a different value) how would I specify which file is which in the dictionary structure that nilearn recognizes? I am used to having a .txt file that indicates the value and name of each region of different rows, is that the same for nilearn?
Thanks in advance!
This network is a functionally defined network, in which regions are organized by their functional network. The region labels are just supposed to be broadly in which regions each ROI lies. So, I would say I have no reason to believe the dictionary provided by Nilearn is incorrect.
You might be interested in making something along the lines of the AAL atlas in Nilearn. In particular, there is an
str, indices mapping ‘labels’ to values in the ‘maps’ image. This list has 116 elements. Since the values in the ‘maps’ image do not correspond to indices in
labels, but rather to values in
indices, the location of a label in the
labels list does not necessary match the associated value in the image. Use the
indices list to identify the appropriate image value for a given label (See main description above).