Most of the values in my array data (difference between conditions) are negative, hence I'm expecting negative T values (which is what I get when I'm not using tfce). However, when using tfce, the test returns only positive T values. Is there a way to overcome this issue?
"By default, voxels that were originally negative will be given zero TFCE score. However, if it is desired to apply the enhancement to both negative and positive values, the original image can be simply negated, passed through the enhancement, re-negated and combined (via addition) with the positive enhancement."
As far as I know, MNE-Python's implementation does *not* do this for you automatically.
-- dan
Daniel McCloy https://dan.mccloy.info
Research Scientist
Institute for Learning and Brain Sciences
University of Washington
Hello,
I am actually wondering if something might be wrong with the way the tfce is performed.
Almost all values in my array are negative, and I get only positive t values. I get exactly the same result when performing a test with tfce on X and on -X. Which means that the sign of the t values is wrong (for some of them at least), unless I'm missing something.
(I'm using 2-tailed cluster permutation test with tfce = dict(start=0., step=0.5)).
Kind regards,
Fleur
It is possible we don't do the re-negation step. Can you open an issue on
GitHub? If you can show the problem on some tiny toy data (3 "subjects", 2
"vertices", 2 "time points"?) that would help.