Here is a link to my data. I've included my pre-processed nifti files, the datamats that I created for two subjects that have been causing me problems. I re-created theses datamats using the "consider all voxels as brain" option. I ran an analysis on these datamats, and it is clear from looking at the result that the brain is empty. Any help as to why this might be happening would be greatly appreciated. https://www.dropbox.com/sh/qh66ghbo59ayqjl/AABmSHIeXhx6EC0JbmadGC7ja

Thanks,

Sabrina

Hi Nancy (and PLS other people),

Thank you for taking a look. The bands appeared because the selected the "consider all voxels as brain" option in creating the datamats for these particular problem subjects. When we created their datamats normally (thresholded at .015), the datamats are empty (and so we cannot run an analysis on them at all). This does not seem to be an error with our pre-processing, because we ran all the participants through the same preprocessing pipeline, and are able to create normal datamats for most of the subjects .

I have now uploaded the data from two subjects for which the datamat files did not create properly ("bad files") and the data from two subjects with which we've had no issues ("good fies"). We preprocessed the scans in the same way and we used the same procedure to create the datamats for both (define brain region automatically: threshold: 0.15 and normalizing data with ref. scans) but you will notice that the datamats for the "bad files" are empty.

https://www.dropbox.com/s/1c6e8vh3741pukt/Updated%20PLS%20problem.zip

In comparing the nifti files, we have not been able to find any difference (the output of 3dinfo is exactly the same). So we can't figure out why the datamats are created for one, but not the other. We must be missing something. If anyone could have a look and see what is causing the issue in the problem files, that would be very helpful. 

Thanks,

Sabrina

It looks like it turned out the be a masking problem. When I appied a group-level MNI mask for the EPI images (instead of thresholding at 0.15 or considering all voxels as brain), the datamats were created just fine. Though my data did have a few voxels with extremely high values, it doesn't seem like my data were completely corrupted. It seems as if normalizing the data through our preprocessing pipeline was not the problem, since analyzing these images (after applying the mask) yeilded resonable results. 

However, since you've taken a look at our data, I was hoping you could confirm just one last thing. We've reviewed the orginal papers, and the website to assure that our preprocessing steps are appropriate for a PLS analysis. Would you mind confirming that everthing we've done is reasonable. Here is an ordered list of what has been done to our data before loading it into the datamats. Please let us know if any of this is incorrect:

- Generated physiological noise regressors

- Slice-time correction

- Motion corrected each subject to the first run of the session

- Created whole brain masks

- Demeaned each dataset

- Freesurfer-derived masks to generate white matter and CSF regressors per run

- Regressed headmotion, linear trend and white matter & CSF out of each run

- FWHM 6mm smoothing

- Linear registration to MNI space

- Analyzed in PLS using group level MNI space mask.

Thank you so much for taking a look at this problem for us, Nancy! Gary, Joe and I really appreciate it.

- Sabrina