After you've been working in science long enough, you may start to discover that the results that you get aren't necessarily the ones that you want. This discrepancy is an abomination, and clearly must be eliminated. One way to do this - at least with FMRI data - is to manually read in a dataset, and overwrite existing values with new ones.
While you can overwrite values in any dataset, I find it helpful to first create a blank dataset that has the same dimensions and orientation as the other data that you are working with. For example, by creating a copy of an existing image and then switching all the values in that dataset to zero. Starting from the ANALYZE files (i.e., .img/.hdr), you will need to convert them to NIFTI before you can use the script; I use AFNI's 3dcopy and then 3dAFNItoNIFTI to do this.
Once you have copied your file, you can zero out the values by using the script createBlankNIFTI.m and then fill in new values using createNIFTI.m. I'm sure these can both be combined somehow in the future, but there isn't a terribly high demand for this capability yet, so I'll leave it as is.
While you can overwrite values in any dataset, I find it helpful to first create a blank dataset that has the same dimensions and orientation as the other data that you are working with. For example, by creating a copy of an existing image and then switching all the values in that dataset to zero. Starting from the ANALYZE files (i.e., .img/.hdr), you will need to convert them to NIFTI before you can use the script; I use AFNI's 3dcopy and then 3dAFNItoNIFTI to do this.
Once you have copied your file, you can zero out the values by using the script createBlankNIFTI.m and then fill in new values using createNIFTI.m. I'm sure these can both be combined somehow in the future, but there isn't a terribly high demand for this capability yet, so I'll leave it as is.