I need to find correlation of 3D+time with a time series I found function 3dTcorr1D which seems to do just that

2 questions:

I need to get the corrcoef along with the significances (or some stat that I can threshold for significance) in the description of function there is no mention of whether p values can be accesses ( e.g. looks like only 1 volume is returned per column of 1D input)

-pearson correlation option is mentioned to be a dot product. Is this calculated after subtracting the averages from to be correlated signals or should we demean the inputs before ourselves?

No detrending, blurring, or other pre-processing options are available;
if you want these things, see 3dDetrend or 3dBandpass or 3dcalc.
[In other words, this program presumes you know what you are doing!]

Gang’s suggestion of 3dDeconvolve or 3dfim+ can get you p-values,
or you could run cdf yourself. For example, if a time series of length 100
yields a correlation of 0.3, then the p-value might be 0.002426, from:

cdf -t2p fico .3 100 1 1

That assumes only demeaning the data (as is done with 3dTcorr1D).
If quadratic detrending were done, the ort-dof would be 3, leading to
a p-value of 0.00269, from:

cdf -t2p fico .3 100 1 3

See the output of “cdf -help” for details.

3dTcorr1D removes the mean, but does no trend removal.

Thank you my friends for suggestions and clarifications. I want to used 3dTcorr1D because I want to do multiple correlations at the same time ( i prefer to avoid for loops). signals to be correlated are concatenated 3D beta values from individual runs and a behavioral measure relevant for each run. so I do not need detrending.

In this case say I use z transform to calculate p values for correlations (probably using 3dcalc), I can then use this pvalue brick to threshold the correlations overlay (unless there is a better solution in afni?). Also may I suggest to add stats to 3dcorr1D in future? no one can report values without stats these days.

I ran 3dTcorr1D and realized it outputs p values that are shown in afni. That is what I was looking for so I do not have to calculate myself.

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