Dear Freesurfers, I have a question regarding normalization of functional activation maps. Let us assume I have functional activation maps on a vertex space (e.g. mgh files reflecting degree centrality, which do not need previous normalization of functional data). After resampling activation maps into individual vertex space (bbregister and vol2surf), I want to investigate if any differences across two groups are detectable regarding this activation maps. Which pipeline/command/stream is better to use for first normalize the activation maps into a common template and then create an output that can be implemented into mri_glmfit ? Should fsfast be used? Or is mri_surf2surf better? Thanks a lot,
Lorenzo
If they are already in fsaverage space, then you just need to concatenate them together (mri_concat) and then use the concat file as input (--y) to mri_glmfit doug
On 10/01/2014 12:09 PM, lorenzo pasquini wrote:
Dear Freesurfers, I have a question regarding normalization of functional activation maps. Let us assume I have functional activation maps on a vertex space (e.g. mgh files reflecting degree centrality, which do not need previous normalization of functional data). After resampling activation maps into individual vertex space (bbregister and vol2surf), I want to investigate if any differences across two groups are detectable regarding this activation maps. Which pipeline/command/stream is better to use for first normalize the activation maps into a common template and then create an output that can be implemented into mri_glmfit ? Should fsfast be used? Or is mri_surf2surf better? Thanks a lot,
Lorenzo
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
freesurfer@nmr.mgh.harvard.edu
-
Douglas N Greve -
lorenzo pasquini