Hi Nicola,

I think I might be able to answer your question partially:

- I believe that the error bars indicate the quantiles (most likely the 0.25 and 0.75 quantile values).
As an example, take two ROIs. The ROI-ROI Fisher transformed correlation coefficients (for my data comprising of seven subjects) are:

y1 = 0.2677 ; -0.0862 ; 0.1108 ; 0.2222 ; 0.3000 ; 0.0448 ; 0.2750

The mean value is 0.1620 which is shown as my beta value in the results (0.16).

Now, if I were to calculate the quantiles of this set of data using the following MATLAB command:

quantile(y1, [0, 0.25, 0.5, 0.75, 1])

I get the following quantiles: -0.0862 ; 0.0613 ; 0.2222 ; 0.2732 ; 0.3000


Now, looking at the effect size graph (see attachment), I note that the blue area is the value from zero to 0.16 (the mean effect in my group) and the upper and lower error bar roughly correspond to 0.27 and 0.06 respectively (and hence the thought that the error bars correspond to these respective quantiles).


- When you right click on the second level results table, you get an option of exporting your stats which include the list of (mean) betas as seen in the table.


- Do explore the variable conn_*/results/secondlevel/ANALYSIS_01/Covariate_name/Condition_name/ROI.mat file. When you import this in MATLAB, you will have a structure with many fields. Some of note are:
y: would have the subject level betas
names: would have the source ROI names
names2: target ROI names
h: the effect size (mean beta)
F: the T stats value (or the stats mentioned in "statsname" entry)

From the ROI.h variable, you should be able to re-construct the Fisher transformed correlation coefficients and re-convert them to raw correlation coefficients (if you want) by using

r = tanh(z)

as suggested/discussed here: https://www.nitrc.org/forum/message.php?msg_id=13686 and here: https://www.nitrc.org/forum/message.php?...


Regards

Pravesh Parekh

Originally posted by Nicola Toschi: