Dear Dr. Alfonso,

I have two questions regarding the p values in Conn. My apologies if these questions are too trivial or the answers obvious.

1. I am exploring the ROI.mat file (conn_*/results/secondlevel/ANALYSIS_01/AllSubjects/Condition_Name/ROI.mat) and I observed the following (ROI-ROI connectivity):

- xX has the details of the subjects selected
- y has the correlation values of all subjects (correlation is the measure of functional connectivity selected at first level)
- names has the names of the source ROIs
- xyz most likely has the centroid of the source ROI
- names2 has the name of the target ROIs
- xyz2 most likely has the centroid location of the traget ROIs
- h is the average of the y values for the particular pair of ROIs (essentially, the beta displayed in the results window)
- F has the appropriate statistical value (T value for example)

(I am assuming that my interpretation of these names is correct)

However, when I look at the p variable, I expect to find the uncorrected p value. However, the displayed value is nowhere close to the actual uncorrected or FDR corrected p value.

To illustrate the above,

ROI(1).y(:,2) gives me the correlation coefficients of all subjects between ROI1 and ROI2 which is:
-0.1660
-0.2309
0.6556
-0.1602
0.2618
-0.0870
0.0671

Now, I use [h, p, ci, stats] = ttest(ROI(1).y(:,2)) to get my statistics

which returns p =0.6982 (the uncorrected p value being displayed in the results window) and T = 0.4069 which corresponds to the T statistics. I find the same values in the h and F variables. However, ROI(1).p(:,2) has a value of 0.3491. What exactly is this p value indicating (the p-FDR in the results window reads 0.8510)?


2. Another question is regarding the calculation of p-FDR value. I understand that the FDR procedure controls for the proportion of false positives. The procedure involves sorting the p values and then finding the rank for which the FDR equation is satisfied. All values that are lower than (or equal to) the p value at that rank are considered significant. Therefore, we are essentially just changing the alpha value. However, the p value obtained from the test should remain the same, right? How should I go about interpreting the p-FDR value? In the above example, if I run the same set of correlation coefficients through a t test (with a different alpha value), my T and p values remain the same (with only a change in the confidence interval).