Transform t-map from z-map

Hi, REST experts

I'm trying to analyse my resting state fMRI data using by REST v1.3.

You know, the results of REST is the z-map. But, I want to get the t-map.

So, how I can transform z-map to t-map? Or, if I can't that, how can I interprete the result of z-map?

Dear Jeehye,
       z-maps of functional connectivity were used in one-sample t test, two-sample t test or paired t test. This is called Fisher's r-to-z transformation in order to improve the data normality for t tests.
       Usually, t maps were created by t tests but not functional connectivity (Pierson's correlation) directly.
       You can find the z-map in lots of Resting-State fMRI functional connectivity studies.
       Hope this informaiton helpful, best wishes for you!

Dear Jeehye,
Is your connectivity map Pierson's correlation across subjects?
Do you just want to interpret the correlation coefficient r?
If so, please find a program from (the forth), it will help you to convert r value to p value, you will know if your correlation is significant.

Thank, I understand ^^
And I identified the r value using rest_sliceviewer.
But, it was the one point value. isn't it?
I want to get the map with p value like z-map.
So, I'm trying to study matlab m-file ^^
Thank you for your help. I have so much information from you.

Hi Chao-Gan
Now, I'm trying to analyze two seed or three seed resting fMRI using REST v1.3.
Have you ever studied about multi-seed ROI user's mask using REST v1.3?
I tested two seed on my individual resting data using REST v1.3, but, the high peak of the result map was on the only one seed.
The other seed was not high peak. Why was that? I'm so wondering about that.

Dear Jeehye,
You need to merge the two seeds in one ROI file.
please try
[Data1, Vox, Head]=rest_readfile('Seed1.img');

[Data2, Vox, Head]=rest_readfile('Seed2.img');

Then perform FC analysis depend on MergedROI.img.

You also can write programs to convert r to p, by using rest_readfile and rest_WriteNiftiImage.

I'm Jeehye

I have a question about the multi-seed resting analysis.

First, I analyzed only one seed for two regions, respectively.
Second, I analyzed the two seed that were avalyzed in first, and I merged the two seeds in one ROI file.

The question I wondered is that how the two seeds are working in program?

Are the two seeds independent? Or dependent?

How is working?

Hi! Jeehye!
If you merged the two seeds into one ROI file, then the mean time courses was averaged from these two regions. The functional connectivity map was the Pierson's correlation between the mean time course and the time course of all the voxels in the brain.