Hi Daniel,
I think your suggestions are very helpful. But I also have two kinds of questions:

1. I have tried to use the TT_desai_ddpmaps atlas.

1.1 Many regions are included in this atlas:

whereami -show_atlas_code -atlas DD_Desai_PM 
++ Input coordinates orientation set by default rules to RAI
Atlas DD_Desai_PM, 150 regions
----------- Begin regions for DD_Desai_PM atlas-----------
u:ctx_lh_G_and_S_frontomargin:0
...
u:ctx_lh_S_central:45
u:ctx_lh_S_cingul-Marginalis:46
u:ctx_lh_S_circular_insula_ant:47
u:ctx_lh_S_circular_insula_inf:48
u:ctx_lh_S_circular_insula_sup:49
...

What's the coding rules of these region codes? I think "ctx" stands for "cortex" and "lh" stands for left hemisphere. But what dose "u:' and 'G' or 'S' stand for in the region names?

1.2 And my ROI was generated using:

whereami -mask_atlas_region DD_Desai_PM:left:ctx_lh_G_insular_short -prefix test

Display of the generated ROI:
It is weird that many different values (range from 0 to 255) are included in the generated mask "test+tlrc", which is very different from the mask generated using the other atlas. I think the different values represent different probability. But how to use the probabilistic mask? Should I use all the voxels with positive values in this mask (That is, all the voxels with the probability bigger than 0 will be included )? Or should I choose a threshold (e.g. 200) and only the voxels with the values bigger than this threshold can be identified as the target ROI (insular_short in my case)?
2. The Brainnetome atlas seems to be very good. But this atlas was still normalized to MNI152 brain template, not the mni version of the N27 dataset, just like the atlas I mentioned. So the adwarp method which moves the N27 dataset from MNI space to Talairach space becomes inappropriate for an MNI152 template? Are there more appropriate ways to transform if I really want to use the Brainnetome atlas?

Sorry for so many questions!
Thank you again!
Yu



Edited 9 time(s). Last edit at 03/05/2018 05:56AM by Zhang Yu.