AFNI Message Board

Dear AFNI users-

We are very pleased to announce that the new AFNI Message Board framework is up! Please join us at:

https://discuss.afni.nimh.nih.gov

Existing user accounts have been migrated, so returning users can login by requesting a password reset. New users can create accounts, as well, through a standard account creation process. Please note that these setup emails might initially go to spam folders (esp. for NIH users!), so please check those locations in the beginning.

The current Message Board discussion threads have been migrated to the new framework. The current Message Board will remain visible, but read-only, for a little while.

Sincerely, AFNI HQ

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neurobie
June 29, 2015 06:54PM
Hi! I've noticed that, at least in literature using the paradigm I'm using, folks tend to use an 8-mm Gaussian kernel when preprocessing w/ SPM & a 4-mm kernel w/ BrainVoyager or AFNI (only one paper used AFNI).

I previously preprocessed my data using a 4-mm kernel but tried again w/ a 6-mm kernel in light of most papers' using bigger. I found that the maps looked tidier & ROI-based results, more robust (e.g., greater correlations w/ behavioral measures in predicted directions), w/ the 4-mm kernel.

As AFNI defaults to a 4-mm kernel using uber_subject.py, I'm now trying to understand why. Might smaller be better for AFNI & bigger for SPM? How come? Finally, what would you recommend should guide my final kernel selection? Thanks for any thoughts! -Robie



Edited 1 time(s). Last edit at 06/29/2015 06:57PM by neurobie.
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Default smoothing kernel using uber_subject.py

neurobie June 29, 2015 06:54PM

Re: Default smoothing kernel using uber_subject.py

Peter Molfese June 29, 2015 11:37PM