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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
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Thanks, Rick.
Yes, demeaning BEFORE the zeros are inserted does not affect the correlation.
However, say I have an errts file, and I extract the time series from certain ROIs. Does this time series also come demeaned before the zeros are inserted (due to scrubbing)?
Additionally, assuming these errts files have a mean of 0, then would the results of dual regression (in FSL) depend on whe
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AFNI Message Board
Thanks for the reply.
Yes, my correlations are within-subject and session so all areas (seeds and targets) will have 0 as values. However, isn't the correlation going to be different for, let's say,
1 5
2 6
3 7
0 0
0 0
5 8
VS
1 5
2 6
3 7
5 8
Since the latter timeseries has the 0's removed? Same happens to the correlation values in my data.
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AFNI Message Board
Dear experts,
I use censoring or scrubbing of volumes as a preprocessing step in my resting-state data. This leads to errts files which have certain volumes as all 0s.
My question is, when I am going to perform functional connectivity analysis with these censored datasets, how do I deal with the 0 values in the time series? Should I change my code such that only non-zero values are correlat
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AFNI Message Board
Has anyone done this before?
What is the standard way to include these convolved slice-based regressors into afni_proc.py?
Thanks in advance.
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AFNI Message Board
Hi Rick,
Thank you for the reply. The basis functions used will be those specified in these papers:
Chang, C., Cunningham, J. P., & Glover, G. H. (2009). Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage, 44(3), 857-869.
Birn, R. M., Smith, M. A., Jones, T. B., & Bandettini, P. A. (2008). The respiration response function: the temporal dynamic
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Hi Rick,
Sorry for the late reply to this.
Yes, the regressors would vary across slices, just like the RETROICOR regressors. I can generate an RVT time series and convolve it with the appropriate kernel (and do the same for the heart rate), but how what would be the correct way to regress out these signals in afni_proc.py?
Thanks in advance.
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Dear AFNI experts,
I am analyzing some resting-state data. I also collected physiological recordings (cardiac and respiratory) for this dataset.
For preprocessing the dataset, I am following this order of blocks on afni_proc.py:
despike > ricor > tshift > align > tlrc > volreg > mask > regress
My ricor regressors are created from RetroTs.py - which outputs 13 colu
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