Python SUITE: AFNI_Tools.gz

This is an update from my earlier post about the Python program convertDICOM2AFNI.py; now it is a folder containing a few programs that interact with each other. The rationale was to give the original dicom conversion script a wrapper that can change directory (since I am currently unable to do this from the python interpreter invoked by the Unix shell). All the user needs to do is download the file AFNI_Tools.gz into their home directory, unzip it using "tar -zxvf AFNI_Tools.gz" into your experimental directory, and run it using the command "tcsh convertRaw2AFNI.sh". The user will be prompted for a list of subject IDs, the name of the experimental directory, and will then run the script convertDICOM2AFNI.py inside the raw fMRI data folder for each subject. This in turn will prompt the user for study-specific info, such as number of slices, number of TRs, and so on. Currently, there is no way to account for runs with different numbers of TRs; I may add this function in the future.

A short screencast demo of the suite is available at www.screencast.com/users/Andrew.Jahn/folders/Jahn_Tools (you may need to go full-screen mode in order to see what I'm typing on the command line). What I did not mention in the demo is the subfolder "Paths" within the AFNI_Tools folder. It contains two text files, groupDir.txt and dataDir.txt. groupDir.txt contains the path to the experimental directory, while dataDir.txt contains the path to the output directory that you want for each subject; these should be modified when you download the suite. I've included some documentation that I hope is enough to get people started.

In addition, after converting the raw scanner files to both AFNI and NIFTI format, the program will drive AFNI directly from the command line and allow the user to interactively scroll through each functional dataset. For example, the first run of functional data will be displayed and a video of its timecourse will start playing; this is a good way to visually check for any scanner artifacts or excessive head motion, and deciding what to do with it. You can then hit enter in the terminal window that generated AFNI, and it will skip to the next functional run of data, and so on. The idea is to make it easy to do a first-pass analysis on your raw data and see whether anything is completely out of line.

As always, please send me feedback if you have actually used the program, and what you think about it.


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P.S. Photos from the AFNI bootcamp are now up. I now have photographic evidence that I was at the National Institutes of Health, and not somewhere else, like Aruba.

AFNI Bootcamp / Proof that I was not in Aruba