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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
History of AFNI updates
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Hi Gang,
We are going to use Granger causality to do the effective connectivity analysis. Some papers suggested that hemodynamic deconvolution is used as a preprocessing step before Granger causality analysis to remove the effect of hemodynamic response. The papers said that hemodynamic deconvolution removes the inter-subject and inter-regional variability of the HRF, as well as its smoothing
by
angelw
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AFNI Message Board
Hi Gang,
Thanks for your advice. Owing to the big caveats, would you more recommend to use the output from "-fitts" as the input for the later connectivity analysis? I will extract the time series of the "fitts" dataset from the selected ROIs for the connectivity analysis. If that's the case, is it not possible to see the differences between the two stimuli in the conn
by
angelw
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AFNI Message Board
Hi,
I would like to use deconvolved time series from selected ROIs as inputs for functional or effective connectivity analysis. I know 3dDeconvolve can be used to calculate the deconvolution of a 3D+time dataset with a specified input stimulus time series. I also know the option "-fitts" can be used to output a dataset which contains the full-model time series fit to the input data.
by
angelw
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AFNI Message Board
Hi AFNI experts,
We are using afni_proc.py to pre-process our resting state fMRI data by following the example 11. We set the motion limit as 0.2 as recommended in the note. We acquired a total of 5 fMRI runs for investigating the dynamic functional connectivity changes across the time, and therefor we don't want to concatenate the runs together. But we found that the motion artifact was
by
angelw
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AFNI Message Board