Do I need to strip off all the additional planes of zeros before matching the fMRI data and the atlas?

[Erick Zhou]

Dear all,

I hope this email finds you well. I am a research scientist from Tulane University.

I have a question about what kind of processing should be done before matching the fMRI data and the atlas. I got a suggestion that “additional planes of zeros should be firstly stripped off before changing the atlas to match the data”. If the Harvard-oxford atlas and the preprocessed functional image have different number of additional planes of zeros. What kind of processing should be done before matching them.

The detailed information is shown as follows:

I downloaded the preprocessed functional image that is processed using C-PAC pipeline, with frequency filtering and global signal regression, from the ABIDE website. (I used the following command to download the preprocessed functional image:

python C:/download_abide_preproc.py -d func_preproc  -p cpac -s filt_global -o D:/ABIDE1_File

I tried to find out the Harvard-oxford atlas having overlapping volumes with the preprocessed functional image that is processed using C-PAC pipeline. However, I only find the Havard-oxford atlas for NIAK pipeline with non- overlapping volumes with the preprocessed functional image from the following link of ABIDE http://preprocessed-connectomes-project.org/abide/Pipelines.html

I loaded the Harvard-oxford atlas into Matlab by using “load_nii” function, and found that more than 16 planes are the planes of zeros in each of x, y, z direction. I also loaded the preprocessed functional image (e.g., Yale_0050626_func_preproc.nii). There are also a few planes of zeros for the preprocessed functional image. But the number of the planes of zeros in the preprocessed functional image is different from the Harvard-oxford atlas.

Best Regards

Zhongxing Zhou

[Cameron Craddock]

Hello Zhongxing,

I sent a reply to another of your emails. Strictly speaking this is not the correct forum to answer your question. If you used CPAC to process your data and to do the extraction, it would handle this problem for you. As I mentioned in my other emails, I think that the best way to solve your problem is to just directly download the extracted time series data from the FCP-INDI bucket using the instructions here: http://preprocessed-connectomes-project.org/abide/download.html. The derivative names for the time series are a bit confusing, but are: rois_aal, rois_cc200, rois_cc400, rois_dosenbach160, rois_ez, rois_ho or rois_tt depending on the atlas you would like to use.

Regards,

Cameron

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