Hi Alfonso,
In addition to the T1 anatomical space I mentioned earlier in this thread, I also tried to perform the fMRI processing analysis in the subject's native diffusion space. In order to do that, I registered my original T1 scan into the subject's diffusion b0 space and then used that as the structural scan in the CONN pipeline. The pipeline I ran was the modified "preprocessing pipeline for surface-based analyses (in subject space)" you kindly suggested in the previous message of this chain (I additionally changed the target structural voxel-size in the preprocessing setup to the diffusion scan's voxel size rather than the default 1mm.)
I had two follow-up questions:
1. Does that methodology sound correct if I want to perform the processing on diffusion space, or is there a more "appropriate" way to do it? After running it for one subject the results seem to make sense (including the segmentations), and the final denoised volume seems to be correctly registered to diffusion space!
2. I would like to assess functional connectivity (i.e., Pearson correlations between BOLD timeseries) between all given voxels within a cortical mask I have (this mask is in diffusion space and each voxel within that mask is assigned a different integer value - in that sense it's not really a binary mask, although I could convert it to one if needed).
I can think of a tedious way of doing that by matching those specific voxels to their equivalent rows within the vvPC_Subject*_Condition*.matc file and then calculating the Pearson correlation, but I was wondering whether there was a more efficient way (such as for instance by adding this mask as an ROI under Setup and then have a matrix file be exported).
Thanks in advance for all your help!
Best,
Panos
In addition to the T1 anatomical space I mentioned earlier in this thread, I also tried to perform the fMRI processing analysis in the subject's native diffusion space. In order to do that, I registered my original T1 scan into the subject's diffusion b0 space and then used that as the structural scan in the CONN pipeline. The pipeline I ran was the modified "preprocessing pipeline for surface-based analyses (in subject space)" you kindly suggested in the previous message of this chain (I additionally changed the target structural voxel-size in the preprocessing setup to the diffusion scan's voxel size rather than the default 1mm.)
I had two follow-up questions:
1. Does that methodology sound correct if I want to perform the processing on diffusion space, or is there a more "appropriate" way to do it? After running it for one subject the results seem to make sense (including the segmentations), and the final denoised volume seems to be correctly registered to diffusion space!
2. I would like to assess functional connectivity (i.e., Pearson correlations between BOLD timeseries) between all given voxels within a cortical mask I have (this mask is in diffusion space and each voxel within that mask is assigned a different integer value - in that sense it's not really a binary mask, although I could convert it to one if needed).
I can think of a tedious way of doing that by matching those specific voxels to their equivalent rows within the vvPC_Subject*_Condition*.matc file and then calculating the Pearson correlation, but I was wondering whether there was a more efficient way (such as for instance by adding this mask as an ROI under Setup and then have a matrix file be exported).
Thanks in advance for all your help!
Best,
Panos